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    <description>recent bookmarks from cshalizi</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://www.cambridge.org/core/elements/boolean-networks-as-predictive-models-of-emergent-biological-behaviors/0D2383F0D64543A77892CEBD5C6A964B"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41557-025-01981-y"/>
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	<rdf:li rdf:resource="https://www.nature.com/articles/d41586-021-00977-1"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1911.10252"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1506.00728"/>
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  </channel><item rdf:about="https://www.cambridge.org/core/elements/boolean-networks-as-predictive-models-of-emergent-biological-behaviors/0D2383F0D64543A77892CEBD5C6A964B">
    <title>Boolean Networks as Predictive Models of Emergent Biological Behaviors</title>
    <dc:date>2026-04-17T03:18:43+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/boolean-networks-as-predictive-models-of-emergent-biological-behaviors/0D2383F0D64543A77892CEBD5C6A964B</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization."]]></description>
<dc:subject>to:NB biochemical_networks of_course_its_really_a_spin_glass books:noted downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98c166445d04/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
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<item rdf:about="https://www.nature.com/articles/s41557-025-01981-y">
    <title>A recursive enzymatic competition network capable of multitask molecular information processing | Nature Chemistry</title>
    <dc:date>2025-12-10T15:44:15+00:00</dc:date>
    <link>https://www.nature.com/articles/s41557-025-01981-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Living cells understand their environment by combining, integrating and interpreting chemical and physical stimuli. Despite considerable advances in the design of enzymatic reaction networks that mimic hallmarks of living systems, these approaches lack the complexity to fully capture biological information processing. Here we introduce a scalable approach to design complex enzymatic reaction networks capable of reservoir computation based on recursive competition of substrates. This protease-based network can perform a broad range of classification tasks based on peptide and physicochemical inputs and can simultaneously perform an extensive set of discrete and continuous information processing tasks. The enzymatic reservoir can act as a temperature sensor from 25 °C to 55 °C with 1.3 °C accuracy, and performs decision-making, activation and tuning tasks common to neurological systems. We show a possible route to temporal information processing and a direct interface with optical systems by demonstrating the extension of the network to incorporate sensitivity to light pulses. Our results show a class of competition-based molecular systems capable of increasingly powerful information-processing tasks."]]></description>
<dc:subject>to:NB biochemical_networks biological_computation via:vaguery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d4b7ca5761e2/</dc:identifier>
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<item rdf:about="https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444">
    <title>Network Analysis as a Grand Unifier in Biomedical Data Science | Annual Reviews</title>
    <dc:date>2025-04-09T14:54:58+00:00</dc:date>
    <link>https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease."]]></description>
<dc:subject>to:NB network_data_analysis networks biochemical_networks via:aks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30e36c1f0a02/</dc:identifier>
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<item rdf:about="https://scienceintegritydigest.com/2024/02/15/the-rat-with-the-big-balls-and-enormous-penis-how-frontiers-published-a-paper-with-botched-ai-generated-images/">
    <title>The rat with the big balls and the enormous penis – how Frontiers published a paper with botched AI-generated images – Science Integrity Digest</title>
    <dc:date>2024-02-24T20:38:56+00:00</dc:date>
    <link>https://scienceintegritydigest.com/2024/02/15/the-rat-with-the-big-balls-and-enormous-penis-how-frontiers-published-a-paper-with-botched-ai-generated-images/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Bookmarking this because it (thoughtfully) includes a full version of the now-retracted paper, so that posterity can appreciate JAK JAK JAK.
--- This was, of course, a peer-reviewed paper in a journal indexed by all the usual suspects.]]></description>
<dc:subject>funny:academic funny:malicious funny:laughing_instead_of_screaming ai_madness biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5fba6228b500/</dc:identifier>
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<item rdf:about="https://www.nature.com/articles/d41586-021-00977-1">
    <title>Life in a carbon dioxide world</title>
    <dc:date>2021-04-24T20:39:46+00:00</dc:date>
    <link>https://www.nature.com/articles/d41586-021-00977-1</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>biochemical_networks evolutionary_biology via:paul_mcauley have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:15719c0774da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
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<item rdf:about="https://arxiv.org/abs/2104.10082">
    <title>[2104.10082] Modeling biological networks: from single gene systems to large microbial communities</title>
    <dc:date>2021-04-21T19:49:52+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10082</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this research, we study biological networks at different scales: a gene autoregulatory network at the single-cell level and the gut microbiota at the population level.
"Proteins are the main actors in cells, they are the building blocks, act as enzymes and antibodies. The production of proteins is mediated by transcription factors. In some cases, a protein acts as its own transcription factor, this is called autoregulation. It is known that autorepression speeds up the response and that autoactivation can lead to multiple stable equilibria. In this thesis, we study the effects of the combination of activation and repression in autoregulation, as a case study we investigate the possible dynamics of the leucine responsive protein B of the archaeon Sulfolobus solfataricus (Ss-LrpB), a protein that regulates itself in a unique and non-monotonic way via three binding boxes. We examine for which conditions this type of network leads to oscillations or bistability.
"In the second part, much larger biological systems are considered. Ecological systems, among which the human gut microbiome, are characterized by heavy-tailed abundance profiles. We study how these distributions can arise from population-based models by adding saturation effects and linear noise. Moreover, we examine different characteristics of experimental time series of microbial communities, such as the noise color and neutrality of the biodiversity, and look at the influence of the parameters on these characteristics. With the first research topic we want to lay a foundation for the understanding of non-monotonic gene regulation and take the first steps toward synthetic biology in archaea. In the second part of the thesis, we investigate experimental time series from complex ecosystems and seek theoretical models reproducing all observed characteristics in view of building predictive models."]]></description>
<dc:subject>to:NB biochemical_networks heavy_tails</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27cd92aa49a4/</dc:identifier>
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<item rdf:about="https://projecteuclid.org/euclid.ss/1608541220">
    <title>Wang , Li , Li , Huang : Network Modeling in Biology: Statistical Methods for Gene and Brain Networks</title>
    <dc:date>2020-12-21T14:09:31+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.ss/1608541220</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The rise of network data in many different domains has offered researchers new insights into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using measured data as a first step. We provide a discussion on existing statistical and computational methods for edge estimation and subsequent statistical inference problems in these two types of biological networks."]]></description>
<dc:subject>to:NB network_data_analysis biochemical_networks neural_data_analysis gene_expression_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4b92635daad/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
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<item rdf:about="https://arxiv.org/abs/1810.10854">
    <title>[1810.10854] Structure learning of undirected graphical models for count data</title>
    <dc:date>2020-11-25T14:54:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.10854</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biological processes underlying the basic functions of a cell involve complex interactions between genes. From a technical point of view, these interactions can be represented through a graph where genes and their connections are, respectively, nodes and edges. The main objective of this paper is to develop a statistical framework for modelling the interactions between genes when the activity of genes is measured on a discrete scale. In detail, we define a new algorithm for learning the structure of undirected graphs, PC-LPGM, proving its theoretical consistence in the limit of infinite observations. The proposed algorithm shows promising results when applied to simulated data as well as to real data."]]></description>
<dc:subject>to:NB graphical_models biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cb5740b4e4f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
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<item rdf:about="https://arxiv.org/abs/2011.10529">
    <title>[2011.10529] Computation capacities of a broad class of signaling networks are higher than their communication capacities</title>
    <dc:date>2020-11-23T17:36:13+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.10529</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Due to structural and functional abnormalities or genetic variations and mutations, there may be dysfunctional molecules within an intracellular signaling network that do not allow the network to correctly regulate its output molecules, such as transcription factors. This disruption in signaling interrupts normal cellular functions and may eventually develop some pathological conditions. In this paper, computation capacity of signaling networks is introduced as a fundamental limit on signaling capability and performance of such networks. The computation capacity measures the maximum number of computable inputs, that is, the maximum number of input values for which the correct functional output values can be recovered from the erroneous network outputs, when the network contains some dysfunctional molecules. This contrasts with the conventional communication capacity that measures instead the maximum number of input values that can be correctly distinguished based on the erroneous network outputs.
"The computation capacity is higher than the communication capacity, if the network response function is not a one-to-one function of the input signals. By explicitly incorporating the effect of signaling errors that result in the network dysfunction, the computation capacity provides more information about the network and its malfunction. Two examples of signaling networks are studied here, one regulating caspase3 and another regulating NFkB, for which computation and communication capacities are analyzed. Higher computation capacities are observed for both networks. One biological implication of this finding is that signaling networks may have more capacity than that specified by the conventional communication capacity metric. The effect of feedback is also studied. In summary, this paper reports findings on a new fundamental feature of the signaling capability of cell signaling networks."]]></description>
<dc:subject>to:NB biochemical_networks robustness information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b29e3e6e4e29/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2011.06478">
    <title>[2011.06478] Master regulators as order parameters of gene expression states</title>
    <dc:date>2020-11-15T21:16:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.06478</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model. It is shown that order parameters of this model can be interpreted as concentrations of master transcription regulators that form concurrent positive feedback loops with a large number of downstream regulated genes. The order parameter free energy then defines an epigenetic landscape in which local minima correspond to stable cell states. The model is applied to gene expression data in the context of hematopoiesis."]]></description>
<dc:subject>to:NB biochemical_networks of_course_its_really_a_spin_glass</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53d94fa460fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.04751">
    <title>[2005.04751] Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours</title>
    <dc:date>2020-11-15T21:15:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.04751</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mathematical approaches from dynamical systems theory are used in a range of fields. This includes biology where they are used to describe processes such as protein-protein interaction and gene regulatory networks. As such networks increase in size and complexity, detailed dynamical models become cumbersome, making them difficult to explore and decipher. This necessitates the application of simplifying and coarse graining techniques in order to derive explanatory insight. Here we demonstrate that Zwanzig-Mori projection methods can be used to arbitrarily reduce the dimensionality of dynamical networks while retaining their dynamical properties. We show that a systematic expansion around the quasi-steady state approximation allows an explicit solution for memory functions without prior knowledge of the dynamics. The approach not only preserves the same steady states but also replicates the transients of the original system. The method also correctly predicts the dynamics of multistable systems as well as networks producing sustained and damped oscillations. Applying the approach to a gene regulatory network from the vertebrate neural tube, a well characterised developmental transcriptional network, identifies features of the regulatory network responsible dfor its characteristic transient behaviour. Taken together, our analysis shows that this method is broadly applicable to multistable dynamical systems and offers a powerful and efficient approach for understanding their behaviour."]]></description>
<dc:subject>to:NB dynamical_systems approximation networks biochemical_networks non-equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3dff9992307a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.03286">
    <title>[2006.03286] Opportunities at the interface of network science and metabolic modelling</title>
    <dc:date>2020-11-15T21:14:47+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.03286</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Metabolism plays a central role in cell physiology as it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimisation principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology."]]></description>
<dc:subject>to:NB biochemical_networks networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4edb0b5767ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1912.00401">
    <title>[1912.00401] Long-time asymptotics of stochastic reaction systems</title>
    <dc:date>2020-11-15T21:14:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1912.00401</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the stochastic dynamics of a system of interacting species in a stochastic environment by means of a continuous-time Markov chain with transition rates depending on the state of the environment. Models of gene regulation in systems biology take this form. We characterise the finite-time distribution of the Markov chain, provide conditions for ergodicity, and characterise the stationary distribution (when it exists) as a mixture of Poisson distributions. The mixture measure is uniquely identified as the law of a fixed point of a stochastic recurrence equation. This recursion is crucial for statistical computation of moments and other distributional features."]]></description>
<dc:subject>to:NB stochastic_processes markov_models biochemical_networks to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3c8dfed1a256/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1911.10252">
    <title>[1911.10252] Biological Regulatory Networks are Minimally Frustrated</title>
    <dc:date>2020-11-15T20:55:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1911.10252</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Characterization of the differences between biological and random networks can reveal the design principles that enable the robust realization of crucial biological functions including the establishment of different cell types. Previous studies, focusing on identifying topological features that are present in biological networks but not in random networks, have, however, provided few functional insights. We use a Boolean modeling framework and ideas from spin glass literature to identify functional differences between five real biological networks and random networks with similar topological features. We show that minimal frustration is a fundamental property that allows biological networks to robustly establish cell types and regulate cell fate choice, and this property can emerge in complex networks via Darwinian evolution. The study also provides clues regarding how the regulation of cell fate choice can go awry in a disease like cancer and lead to the emergence of aberrant cell types."]]></description>
<dc:subject>biochemical_networks biophysics of_course_its_really_a_spin_glass in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2225e103413d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7rkfj">
    <title>First Signals: The Evolution of Multicellular Development on JSTOR</title>
    <dc:date>2020-01-26T16:55:06+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7rkfj</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted downloaded developmental_biology evolutionary_biology biochemical_networks bonner.john_tyler in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b02b7bb6a1f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:developmental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bonner.john_tyler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045004">
    <title>Rev. Mod. Phys. 91, 045004 (2019) - Nonequilibrium physics in biology</title>
    <dc:date>2020-01-12T22:59:58+00:00</dc:date>
    <link>https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045004</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Life is characterized by a myriad of complex dynamic processes allowing organisms to grow, reproduce, and evolve. Physical approaches for describing systems out of thermodynamic equilibrium have been increasingly applied to living systems, which often exhibit phenomena not found in those traditionally studied in physics. Spectacular advances in experimentation during the last decade or two, for example, in microscopy, single-cell dynamics, in the reconstruction of subcellular and multicellular systems outside of living organisms, and in high throughput data acquisition, have yielded an unprecedented wealth of data on cell dynamics, genetic regulation, and organismal development. These data have motivated the development and refinement of concepts and tools to dissect the physical mechanisms underlying biological processes. Notably, landscape and flux theory as well as active hydrodynamic gel theory have proven useful in this endeavor. Together with concepts and tools developed in other areas of nonequilibrium physics, significant progress has been made in unraveling the principles underlying efficient energy transport in photosynthesis, cellular regulatory networks, cellular movements and organization, embryonic development and cancer, neural network dynamics, population dynamics and ecology, as well as aging, immune responses, and evolution. Here recent advances in nonequilibrium physics are reviewd and their application to biological systems is surveyed. Many of these results are expected to be important cornerstones as the field continues to build our understanding of life."]]></description>
<dc:subject>to:NB physics biology biophysics biochemical_networks non-equilibrium thermodynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fd84511c14b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thermodynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.ps/1572509200">
    <title>Robert : Mathematical models of gene expression</title>
    <dc:date>2019-11-01T00:52:15+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.ps/1572509200</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we analyze the equilibrium properties of a large class of stochastic processes describing the fundamental biological process within bacterial cells, the production process of proteins. Stochastic models classically used in this context to describe the time evolution of the numbers of mRNAs and proteins are presented and discussed. An extension of these models, which includes elongation phases of mRNAs and proteins, is introduced. A convergence result to equilibrium for the process associated to the number of proteins and mRNAs is proved and a representation of this equilibrium as a functional of a Poisson process in an extended state space is obtained. Explicit expressions for the first two moments of the number of mRNAs and proteins at equilibrium are derived, generalizing some classical formulas. Approximations used in the biological literature for the equilibrium distribution of the number of proteins are discussed and investigated in the light of these results. Several convergence results for the distribution of the number of proteins at equilibrium are in particular obtained under different scaling assumptions."]]></description>
<dc:subject>to:NB gene_expression_data_analysis biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cbd6f3a07c07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.12825">
    <title>[1909.12825] Dynamics of continuous time Markov chains with applications to stochastic reaction networks</title>
    <dc:date>2019-10-15T18:21:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.12825</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper contributes to an in-depth study of properties of continuous time Markov chains (CTMCs) on non-negative integer lattices, with particular interest in one-dimensional CTMCs with polynomial transitions rates. Such stochastic processes are abundant in applications, in particular within biology.
"We study the classification of states for general CTMCs on the non-negative integer lattices, by characterizing the set of absorbing states (similarly, trapping, escaping, positive irreducible components and quasi-irreducible components). For CTMCs on non-negative integers with polynomial transition rates, we provide threshold checkable criteria (in terms of specific easily computable parameters) for various dynamical properties such as explosivity, recurrence vs transience, positive vs null recurrence, implosivity, and existence and non-existence of passage times. In particular, checkable sufficient conditions for exponential ergodicity of stationary distributions and quasi-stationary distributions are obtained. Moreover, an identity for stationary measures is established and the asymptotics of the tails of stationary distributions is established. A similar identity as well as asymptotics is derived for quasi-stationary distributions. Finally, we apply our results to some stochastic reaction networks."]]></description>
<dc:subject>to:NB markov_models biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2133a43c6e6f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.11070">
    <title>[1909.11070] Spin Glass Theory of Interacting Metabolic Networks</title>
    <dc:date>2019-09-26T18:06:23+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.11070</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We cast the metabolism of interacting cells within a statistical mechanics framework considering both, the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin vectors, whose values will be constrained by the stochiometry and the energy requirements of the metabolism. Within this picture, finding the phenotypic states of the population turns out to be equivalent to searching for the equilibrium states of a disordered spin model. We provide a general solution of this problem for arbitrary metabolic networks and interactions. We apply this solution to a simplified model of metabolism and to a complex metabolic network, the central core of the \emph{E. coli}, and demonstrate that the combination of selective pressure and interactions define a complex phenotypic space. Cells may specialize in producing or consuming metabolites complementing each other at the population level and this is described by an equilibrium phase space with multiple minima, like in a spin-glass model."]]></description>
<dc:subject>biochemical_networks of_course_its_really_a_spin_glass in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8e1e1e5f0c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.09551">
    <title>[1907.09551] Cell differentiation: what have we learned in 50 years?</title>
    <dc:date>2019-09-15T14:43:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.09551</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I revisit two theories of cell differentiation in multicellular organisms published a half-century ago, Stuart Kauffman's global gene regulatory dynamics (GGRD) model and Roy Britten's and Eric Davidson's modular gene regulatory network (MGRN) model, in light of newer knowledge of mechanisms of gene regulation in the metazoans (animals). The two models continue to inform hypotheses and computational studies of differentiation of lineage-adjacent cell types. However, their shared notion (based on bacterial regulatory systems) of gene switches and networks built from them, have constrained progress in understanding the dynamics and evolution of differentiation. Recent work has described unique write-read-rewrite chromatin-based expression encoding in eukaryotes, as well metazoan-specific processes of gene activation and silencing in condensed-phase, enhancer-recruiting regulatory hubs, employing disordered proteins, including transcription factors, with context-dependent identities. These findings suggest an evolutionary scenario in which the origination of differentiation in animals, rather than depending exclusively on adaptive natural selection, emerged as a consequence of a type of multicellularity in which the novel metazoan gene regulatory apparatus was readily mobilized to amplify and exaggerate inherent cell functions of unicellular ancestors. The plausibility of this hypothesis is illustrated by the evolution of the developmental role of Grainyhead-like in the formation of epithelium."]]></description>
<dc:subject>developmental_biology evolutionary_biology gene_regulation biochemical_networks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cde25938d027/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:developmental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.00038">
    <title>[1909.00038] Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks</title>
    <dc:date>2019-09-04T15:28:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.00038</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Stochastic gene regulatory networks with bursting dynamics can be modeled mesocopically as a generalized density-dependent Markov chain (GDDMC) or macroscopically as a piecewise-deterministic Markov process (PDMP). Here we prove a limit theorem showing that each family of GDDMCs will converge to a PDMP as the system size tends to infinity. Moreover, under a simple dissipative condition, we prove the existence and uniqueness of the stationary distribution and the exponential ergodicity for the PDMP limit via the coupling method. Further extensions and applications to single-cell stochastic gene expression kinetics and bursty stochastic gene regulatory networks are also discussed and the convergence of the stationary distribution of the GDDMC model to that of the PDMP model is also proved."]]></description>
<dc:subject>to:NB biochemical_networks stochastic_processes markov_models re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff5c3428890d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.00042">
    <title>[1909.00042] Macroscopic limits, analytical distributions, and noise structure for stochastic gene expression with coupled feedback loops</title>
    <dc:date>2019-09-04T15:27:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.00042</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Gene expression in individual cells is an inherently stochastic process with large fluctuations. Here we present a comprehensive analysis for stochastic gene expression kinetics in a minimal coupled gene circuit with positive-plus-negative feedback. Our theory unifies and generalizes the discrete and continuous gene expression models proposed previously by viewing the latter as various macroscopic limits of the former. Two types of macroscopic limits are obtained: the Kurtz limit applies to proteins with large burst frequencies and the Lévy limit applies to proteins with large burst sizes. We also derive the analytic steady-state distributions of the protein abundance for both the discrete chemical master equation model and its two macroscopic limits. Furthermore, we obtain the analytic time-dependent distribution of the protein concentration for the classical Friedman-Cai-Xie random bursting model. Our analytic results reveal a strong synergistic interaction between positive and negative feedback loops and a critical phase-transition-like phenomenon in the regime of slow promoter switching. Our theory is also applied to study the intrinsic noise structure of stochastic gene expression in coupled gene circuits and a complete decomposition of noise in terms of five different biophysical origins is provided."]]></description>
<dc:subject>to:NB biochemical_networks gene_expression_data_analysis markov_models stochastic_processes re:almost_none</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ca8c6250ee0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06872">
    <title>[1908.06872] A Modified Ising Model of Barabási-Albert Network with Gene-type Spins</title>
    <dc:date>2019-08-20T15:53:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06872</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to emerging behavior of biological systems. Here we will systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. Hence we will focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose mean-field solution of modified Ising model of a network type that closely resembles real-world network, the Barabási-Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented here can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. This is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size."

--- "We show that asymmetric Ising models show similarities to symmetric Ising models with external field": no doubt!]]></description>
<dc:subject>to:NB statistical_mechanics biochemical_networks networks of_course_its_really_a_spin_glass color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ccb8deafb587/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.09410">
    <title>[1906.09410] A reaction network scheme which implements inference and learning for Hidden Markov Models</title>
    <dc:date>2019-08-20T15:27:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.09410</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the "Expectation" step and the "Maximization" step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm."]]></description>
<dc:subject>to:NB em_algorithm markov_models state-space_models biochemical_networks wiuf.carsten pointless_but_nonetheless_awesome biological_computers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:df0502950c8a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:em_algorithm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:state-space_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiuf.carsten"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pointless_but_nonetheless_awesome"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.05483">
    <title>[1908.05483] Algebraic Coarse-Graining of Biochemical Reaction Networks</title>
    <dc:date>2019-08-16T20:37:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.05483</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biological systems exhibit processes on a wide range of time and length scales. This work demonstrates that models, wherein the interaction between system constituents is captured by algebraic operations, inherently allow for successive coarse-graining operations through quotients of the algebra. Thereby, the class of model is retained and all possible coarse-graining operations are encoded in the lattice of congruences of the model. We analyze a class of algebraic models generated by the subsequent and simultaneous catalytic functions of chemicals within a reaction network. Our ansatz yields coarse-graining operations that cover the network with local functional patches and delete the information about the environment, and complementary operations that resolve only the large-scale functional structure of the network. Finally, we present a geometric interpretation of the algebraic models through an analogy with classical models on vector fields. We then use the geometric framework to show how a coarse-graining of the algebraic model naturally leads to a coarse-graining of the state-space. The framework developed here is aimed at the study of the functional structure of cellular reaction networks spanning a wide range of scales."]]></description>
<dc:subject>to:NB biochemical_networks algebra</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ddef55a1c35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.04642">
    <title>[1908.04642] Semigroup Models for Biochemical Reaction Networks</title>
    <dc:date>2019-08-16T20:37:08+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.04642</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The CRS (chemical reaction system) formalism by Hordijk and Steel is a versatile method to model self-sustaining biochemical reaction networks. Its distinguishing feature is the explicit assignment of catalytic function to chemicals that are part of the network. In this work, we show the introduction of subsequent and simultaneous catalytic functions gives rise to an algebraic structure of a semigroup with additional compatible data of idempotent addition and a partial order. The aim of this paper is to demonstrate that such semigroup models are a natural setup to treat the emergence of autocatalytic biochemical reaction networks. We establish the basic algebraic properties of the algebraic models and show that it is natural to define the function of any subnetwork on the whole reaction network in a mathematically correct way. This leads to a natural discrete dynamics on the network, which results from iteratively considering the self-action on a subnetwork by its own function. Finally, we demonstrate that the identification of the maximal self-sustaining subnetwork of any reaction network is very straightforward in our setup. This leads to an algebraic characterization of the lattice of self-sustaining subsets for any CRS. Moreover, we show that algebraic models for reaction networks with a self-sustaining subnetwork cannot be nilpotent, thus establishing a link to the combinatorial theory of finite semigroups."]]></description>
<dc:subject>to:NB biochemical_networks algebra</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:356fc1ae5eec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.05575">
    <title>[1810.05575] Joining and decomposing reaction networks</title>
    <dc:date>2019-08-16T19:55:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.05575</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In systems and synthetic biology, much research has focused on the behavior and design of single pathways, while, more recently, experimental efforts have focused on how cross-talk (coupling two or more pathways) or inhibiting molecular function (isolating one part of the pathway) affects systems-level behavior. However, the theory for tackling these larger systems in general has lagged behind. Here, we analyze how joining networks (e.g., cross-talk) or decomposing networks (e.g., inhibition or knock-outs) affects three properties that reaction networks may possess---identifiability (recoverability of parameter values from data), steady-state invariants (relationships among species concentrations at steady state, used in model selection), and multistationarity (capacity for multiple steady states, which correspond to multiple cell decisions). Specifically, we prove results that clarify, for a network obtained by joining two smaller networks, how properties of the smaller networks can be inferred from or can imply similar properties of the original network. Our proofs use techniques from computational algebraic geometry, including elimination theory and differential algebra."]]></description>
<dc:subject>to:NB biochemical_networks algebra dynamical_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:20af1837520b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.09841">
    <title>[1907.09841] Coherent feedforward loops can be used to approximately compute positive log-likelihood ratio for detecting persistent signals</title>
    <dc:date>2019-07-24T14:14:17+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.09841</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Living cells need to distinguish persistent signals from transient ones. There is few work on studying persistent detection, in the context of cell signalling, from a stochastic signal point of view. This paper aims to address this gap. This paper considers a persistence detection problem defined over a reaction pathway consisting of three species: an inducer, a transcription factor (TF) and a gene, where the inducer can activate the TF and an active TF can bind to the gene promoter. We model the pathway using chemical master equation so the counts of bound promoters over time is a stochastic signal. We consider the problem of using the continuous-time stochastic signal of the counts of bound promoters to infer whether the inducer signal is persistent or not. We use statistical detection theory to derive the solution to this detection problem, which is to compute the log-likelihood ratio of observing a persistent signal to a transient one. We then show that, if the input is persistent, then the positive log-likelihood ratio can be approximately computed by using the continuous-time signals of the number of active TF molecules and the number of bound promoters. Finally, we show how we can use a coherent feedforward loop to approximately compute this log-likelihood ratio."]]></description>
<dc:subject>to:NB biochemical_networks biological_computation signal_transduction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb722ba8b1ed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/115/5/903">
    <title>Probabilistic switching circuits in DNA | PNAS</title>
    <dc:date>2018-05-07T16:41:12+00:00</dc:date>
    <link>http://www.pnas.org/content/115/5/903</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A natural feature of molecular systems is their inherent stochastic behavior. A fundamental challenge related to the programming of molecular information processing systems is to develop a circuit architecture that controls the stochastic states of individual molecular events. Here we present a systematic implementation of probabilistic switching circuits, using DNA strand displacement reactions. Exploiting the intrinsic stochasticity of molecular interactions, we developed a simple, unbiased DNA switch: An input signal strand binds to the switch and releases an output signal strand with probability one-half. Using this unbiased switch as a molecular building block, we designed DNA circuits that convert an input signal to an output signal with any desired probability. Further, this probability can be switched between 2n different values by simply varying the presence or absence of n distinct DNA molecules. We demonstrated several DNA circuits that have multiple layers and feedback, including a circuit that converts an input strand to an output strand with eight different probabilities, controlled by the combination of three DNA molecules. These circuits combine the advantages of digital and analog computation: They allow a small number of distinct input molecules to control a diverse signal range of output molecules, while keeping the inputs robust to noise and the outputs at precise values. Moreover, arbitrarily complex circuit behaviors can be implemented with just a single type of molecular building block."]]></description>
<dc:subject>computation biochemical_networks biological_computers molecular_biology in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0e9c878454d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:molecular_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nature/journal/v537/n7622/full/nature19776.html">
    <title>Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions : Nature : Nature Research</title>
    <dc:date>2016-10-02T19:53:03+00:00</dc:date>
    <link>http://www.nature.com/nature/journal/v537/n7622/full/nature19776.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Networks of organic chemical reactions are important in life and probably played a central part in its origin1, 2, 3. Network dynamics regulate cell division4, 5, 6, circadian rhythms7, nerve impulses8 and chemotaxis9, and guide the development of organisms10. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics11 such as spontaneous pattern formation, bistability and periodic oscillations12, 13, 14, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate–thioester exchange, thiolate–disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes15 and DNA16, 17) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov–Zhabotinskii-type reactions)18, 19, the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving chemical systems."]]></description>
<dc:subject>to:NB biochemical_networks pattern_formation non-equilibrium chemistry physics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b32d9fb753af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pattern_formation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/10285.html">
    <title>Del Vecchio, D. and Murray, R.M.: Biomolecular Feedback Systems (eBook and Hardcover).</title>
    <dc:date>2016-07-25T13:53:13+00:00</dc:date>
    <link>http://press.princeton.edu/titles/10285.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book provides an accessible introduction to the principles and tools for modeling, analyzing, and synthesizing biomolecular systems. It begins with modeling tools such as reaction-rate equations, reduced-order models, stochastic models, and specific models of important core processes. It then describes in detail the control and dynamical systems tools used to analyze these models. These include tools for analyzing stability of equilibria, limit cycles, robustness, and parameter uncertainty. Modeling and analysis techniques are then applied to design examples from both natural systems and synthetic biomolecular circuits. In addition, this comprehensive book addresses the problem of modular composition of synthetic circuits, the tools for analyzing the extent of modularity, and the design techniques for ensuring modular behavior. It also looks at design trade-offs, focusing on perturbations due to noise and competition for shared cellular resources."]]></description>
<dc:subject>to:NB books:noted biochemical_networks feedback biology networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2646e721516e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:feedback"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/academic/subjects/life-sciences/genomics-bioinformatics-and-systems-biology/inner-workings-life-vignettes-systems-biology?format=PB">
    <title>The Inner Workings of Life | Genomics Bioinformatics and Systems Biology | Cambridge University Press</title>
    <dc:date>2016-05-25T01:25:56+00:00</dc:date>
    <link>http://www.cambridge.org/us/academic/subjects/life-sciences/genomics-bioinformatics-and-systems-biology/inner-workings-life-vignettes-systems-biology?format=PB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Living systems are dynamic and extremely complex and their behaviour is often hard to predict by studying their individual parts. Systems biology promises to reveal and analyse these highly connected, regulated and adaptable systems, using mathematical modelling and computational analysis. This new systems approach is already having a broad impact on biological research and has potentially far-reaching implications for our understanding of life. Written in an informal and non-technical style, this book provides an accessible introduction to systems biology. Self-contained vignettes each convey a key theme and are intended to enlighten, provoke and interest readers of different academic disciplines, but also to offer new insight to those working in the field. Using a minimum amount of jargon and no mathematics, Voit manages to convey complex ideas and give the reader a genuine sense of the excitement that systems biology brings with it, as well as the current challenges and opportunities."]]></description>
<dc:subject>to:NB books:noted biology biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:313ddae53dc9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1605.01624">
    <title>[1605.01624] Complete integrability of information processing by biochemical reactions</title>
    <dc:date>2016-05-10T16:52:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.01624</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-)cooperative collective behaviors} in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling -- based on spin systems -- has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis--Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy -- based on completely integrable hydrodynamic-type systems of PDEs -- which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions."]]></description>
<dc:subject>to:NB biochemical_networks biology signal_transduction statistics via:vaguery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:28018146d288/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:vaguery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1506.00728">
    <title>[1506.00728] Network Assisted Analysis to Reveal the Genetic Basis of Autism</title>
    <dc:date>2015-07-14T13:43:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.00728</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk."]]></description>
<dc:subject>to:NB graphical_models biochemical_networks genetics gene_expression_data_analysis autism kith_and_kin roeder.kathryn lei.jing liu.li</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14f7da5f061f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:roeder.kathryn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lei.jing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:liu.li"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nautil.us/issue/20/creativity/the-strange-inevitability-of-evolution">
    <title>The Strange Inevitability of Evolution - Issue 20: Creativity - Nautilus</title>
    <dc:date>2015-01-23T13:18:18+00:00</dc:date>
    <link>http://nautil.us/issue/20/creativity/the-strange-inevitability-of-evolution</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Nice popularization by Philip Ball about neutral networks in evolution, and how they contribute to both robustness and finding innovations.  It's obviously very strongly based on talking with Andreas (so, e.g., no mention of Gerhart and Kirshner!), but not crazily so.]]></description>
<dc:subject>evolutionary_biology biochemical_networks popular_science wagner.andreas schuster.peter have_read via:henry_farrell blogged</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e5d44b3064c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:popular_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wagner.andreas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:schuster.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogged"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/44/15705.abstract.html">
    <title>Transplantation of prokaryotic two-component signaling pathways into mammalian cells</title>
    <dc:date>2014-11-05T18:31:54+00:00</dc:date>
    <link>http://www.pnas.org/content/111/44/15705.abstract.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Signaling pathway engineering is a promising route toward synthetic biological circuits. Histidine–aspartate phosphorelays are thought to have evolved in prokaryotes where they form the basis for two-component signaling. Tyrosine-serine–threonine phosphorelays, exemplified by MAP kinase cascades, are predominant in eukaryotes. Recently, a prokaryotic two-component pathway was implemented in a plant species to sense environmental trinitrotoluene. We reasoned that “transplantation” of two-component pathways into mammalian host could provide an orthogonal and diverse toolkit for a variety of signal processing tasks. Here we report that two-component pathways could be partially reconstituted in mammalian cell culture and used for programmable control of gene expression. To enable this reconstitution, coding sequences of histidine kinase (HK) and response regulator (RR) components were codon-optimized for human cells, whereas the RRs were fused with a transactivation domain. Responsive promoters were furnished by fusing DNA binding sites in front of a minimal promoter. We found that coexpression of HKs and their cognate RRs in cultured mammalian cells is necessary and sufficient to strongly induce gene expression even in the absence of pathways’ chemical triggers in the medium. Both loss-of-function and constitutive mutants behaved as expected. We further used the two-component signaling pathways to implement two-input logical AND, NOR, and OR gene regulation. Thus, two-component systems can be applied in different capacities in mammalian cells and their components can be used for large-scale synthetic gene circuits."]]></description>
<dc:subject>to:NB molecular_biology biochemical_networks computation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f84b9f78ee74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:molecular_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sss.sagepub.com/content/44/4/555.abstract?etoc">
    <title>Multivariate statistics and the enactment of metabolic complexity</title>
    <dc:date>2014-07-29T14:59:49+00:00</dc:date>
    <link>http://sss.sagepub.com/content/44/4/555.abstract?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This ethnographic study, based on fieldwork at the Computational and Systems Medicine laboratory at Imperial College London, shows how researchers in the field of metabolomics – the post-genomic study of the molecules and processes that make up metabolism – enact and coproduce complex views of biology with multivariate statistics. From this data-driven science, metabolism emerges as a multiple, informational and statistical object, which is both produced by and also necessitates particular forms of data production and analysis. Multivariate statistics emerge as ‘natural’ and ‘correct’ ways of engaging with a metabolism that is made up of many variables. In this sense, multivariate statistics allow researchers to engage with and conceptualize metabolism, and also disease and processes of life, as complex entities. Consequently, this article builds on studies of scientific practice and visualization to examine data as material objects rather than black-boxed representations. Data practices are not merely the technological components of experimentation, but are simultaneously technologies and methods and are intertwined with ways of seeing and enacting the biological world. Ultimately, this article questions the increasing invocation and role of complexity within biology, suggesting that discourses of complexity are often imbued with reductionist and determinist ways of thinking about biology, as scientists engage with complexity in calculated and controlled, but also limited, ways."]]></description>
<dc:subject>to:NB to_read ethnography science_as_a_social_process biochemical_networks biology statistics complexity data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:944c59a7a680/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ethnography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.0063">
    <title>[1406.0063] Causal network inference using biochemical kinetics</title>
    <dc:date>2014-07-12T00:23:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.0063</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network models are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of these systems are generally nonlinear, suggesting that suitable nonlinear formulations may offer gains with respect to network inference and associated prediction problems. We present a general framework for both network inference and dynamical prediction that is rooted in nonlinear biochemical kinetics. This is done by considering a dynamical system based on a chemical reaction graph and associated kinetics parameters. Inference regarding both parameters and the reaction graph itself is carried out within a fully Bayesian framework. Prediction of dynamical behavior is achieved by averaging over both parameters and reaction graphs, allowing prediction even when the underlying reactions themselves are unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that nonlinear formulations can yield gains in network inference and permit dynamical prediction in the challenging setting where the reaction graph is unknown."]]></description>
<dc:subject>to:NB biochemical_networks graphical_models statistics estimation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3071dc3df897/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1404.1181">
    <title>[1404.1181] Irreversible thermodynamics of open chemical networks I: Emergent cycles and broken conservation laws</title>
    <dc:date>2014-04-20T18:25:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1404.1181</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this and a companion paper we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks ``in a box'', whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated to nonvanishing affinities, whose symmetries are dictated by the breakage of conservation laws. We decompose the steady state entropy production rate in terms of fundamental fluxes and affinities in the spirit of Schnakenberg's theory of network thermodynamics, paving the way for the forthcoming treatment of the linear regime, of efficiency and tight coupling, of free energy transduction and of thermodynamic constraints for network reconstruction."]]></description>
<dc:subject>to:NB non-equilibrium statistical_mechanics chemistry biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f268f6e05b15/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.0657">
    <title>[1309.0657] Human Genome Variation and the concept of Genotype Networks</title>
    <dc:date>2013-12-16T06:26:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.0657</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Genotype networks are a method used in systems biology to study the 'innovability' of a set of genotypes having the same phenotype. In the past they have been applied to determine the genetic heterogeneity, and stability to mutations, of systems such as metabolic networks and RNA folds. Recently, they have been the base for re-conciliating the two neutralist and selectionist schools on evolution. 
"Here, we adapted the concept of genotype networks to the study of population genetics data, applying them to the 1000 Genomes dataset. We used networks composed of short haplotypes of Single Nucleotide Variants (SNV), and defined phenotypes as the presence or absence of a haplotype in a human population. We used coalescent simulations to determine if the number of samples in the 1000 Genomes dataset is large enough to represent the genetic variation of real populations. The result is a scan of how properties related to the genetic heterogeneity and stability to mutations are distributed along the human genome. We found that genes involved in acquired immunity, such as some HLA and MHC genes, tend to have the most heterogeneous and connected networks, and that coding regions tend to be more heterogeneous and stable to mutations than non-coding regions. We have also found, using coalescent simulations, that regions under selection have more extended and connected networks. 
"In the future, genotype networks may be applied to clinical data, allowing to better understand the innovability of traits related to genetic diseases. However, this possibility is currently limited, because in order to apply genotype networks, we require large datasets of sequencing data. Here we present a framework to apply genotype networks to one of the largest datasets of sequencing data available, and determine to which resolution it is enough to understand variation in the human genome using genotype networks."]]></description>
<dc:subject>to:NB gene_regulation biochemical_networks human_genetics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c2196e489e29/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_genetics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/110/39/E3704.abstract">
    <title>Decisions on the fly in cellular sensory systems</title>
    <dc:date>2013-10-21T15:45:36+00:00</dc:date>
    <link>http://www.pnas.org/content/110/39/E3704.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cell-signaling pathways are often presumed to convert just the level of an external stimulus to response. However, in contexts such as the immune system or rapidly developing embryos, cells plausibly have to make rapid decisions based on limited information. Statistical theory defines absolute bounds on the minimum average observation time necessary for decisions subject to a defined error rate. We show that common genetic circuits have the potential to approach the theoretical optimal performance. They operate by accumulating a single chemical species and then applying a threshold. The circuit parameters required for optimal performance can be learned by a simple hill-climbing search. The complex but reversible protein modifications that accompany signaling thus have the potential to perform analog computations."]]></description>
<dc:subject>to:NB biochemical_networks decision_theory signal_transduction biological_computation biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1d93863b9998/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/discover/10.1086/673209">
    <title>Building Simulations from the Ground Up: Modeling and Theory in Systems Biology [JSTOR: Philosophy of Science, Vol. 80, No. 4 (October 2013), pp. 533-556]</title>
    <dc:date>2013-10-11T22:17:19+00:00</dc:date>
    <link>http://www.jstor.org/discover/10.1086/673209</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory."]]></description>
<dc:subject>to:NB simulation biochemical_networks biology philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f269e9bb94a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.6849">
    <title>[1309.6849] Cyclic Causal Discovery from Continuous Equilibrium Data</title>
    <dc:date>2013-09-27T16:48:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.6849</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we also propose a novel way of modeling interventions that modify the activity of compounds instead of their abundance. For computational reasons, we approximate the nonlinear causal mechanisms by (coupled) local linearizations, one for each experimental condition. We apply the method to reconstruct a cellular signaling network from the flow cytometry data measured by Sachs et al. (2005). We show that our method finds evidence in the data for feedback loops and that it gives a more accurate quantitative description of the data at comparable model complexity."]]></description>
<dc:subject>to:NB causal_inference graphical_models statistics biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:95094aa8a2a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.6066">
    <title>[1309.6066] Bridging physiological and evolutionary time scales in a gene regulatory network</title>
    <dc:date>2013-09-26T19:53:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.6066</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Both physiological response and evolutionary adaptation modify the phenotype, but they act at different time scales. Because gene regulatory networks (GRN) govern phenotypic adaptations, they reflect the trade-offs between these different forces. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN has influenced its evolution. We examined the responses of 32,423 expressed sequences to drought and to the hormone abscisic acid and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian Graphical model and a Random Forest algorithm and studied the genetic diversity of its nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that this function is key in sunflower physiological response to drought. Among Helianthus populations, we observed that more highly connected nodes in the GRN had lower genetic diversity. This systems biology approach combined molecular data at different time scales and identified important physiological processes. At the evolutionary level, we propose that network topology constrained adaptation to dry environment and thus speciation."]]></description>
<dc:subject>to:NB evolutionary_biology gene_regulation biochemical_networks bioinformatics sunflowers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:df8f60743abc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sunflowers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencemag.org/content/341/6145/558.full?rss=1">
    <title>Robustness and Compensation of Information Transmission of Signaling Pathways</title>
    <dc:date>2013-09-03T13:35:26+00:00</dc:date>
    <link>http://www.sciencemag.org/content/341/6145/558.full?rss=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor–mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells."]]></description>
<dc:subject>to:NB biochemical_networks biological_computation information_theory signal_transduction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec8936a928a8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html">
    <title>Dynamic regulatory network controlling TH17 cell differentiation : Nature : Nature Publishing Group</title>
    <dc:date>2013-04-25T01:58:47+00:00</dc:date>
    <link>http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based perturbation tools to systematically derive and experimentally validate a model of the dynamic regulatory network that controls the differentiation of mouse TH17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The TH17 transcriptional network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, the coupled action of which may be essential for maintaining the balance between TH17 and other CD4+ T-cell subsets. Our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles; it also highlights novel drug targets for controlling TH17 cell differentiation."]]></description>
<dc:subject>to:NB biochemical_networks gene_expression_data_analysis immunology molecular_biology experimental_biology bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc937a6155af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:immunology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:molecular_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bioinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoas/1365527204">
    <title>Li , Hsu , Peng , Wang : Bootstrap inference for network construction with an application to a breast cancer microarray study</title>
    <dc:date>2013-04-10T15:51:36+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoas/1365527204</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension–low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or cross-validation often do not work well. In this paper, we propose a new method—Bootstrap Inference for Network COnstruction (BINCO)—to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify high-confidence edges and well-connected hub genes that could potentially play important roles in understanding the underlying biological processes of breast cancer."]]></description>
<dc:subject>to:NB network_data_analysis bootstrap biochemical_networks gene_expression_data_analysis hypothesis_testing multiple_testing re:6dfb</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aadf9efd17d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:multiple_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:6dfb"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.6548">
    <title>[1302.6548] Spatial partitioning improves the reliability of biochemical signaling</title>
    <dc:date>2013-03-05T22:32:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.6548</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Spatial heterogeneity is a hallmark of living systems, even at the molecular scale in individual cells. A key example is the partitioning of membrane-bound proteins via lipid domain formation or cytoskeleton-induced corralling. Yet the impact of this spatial heterogeneity on biochemical signaling processes is poorly understood. Here we demonstrate that partitioning improves the reliability of biochemical signaling. We exactly solve a stochastic model describing a ubiquitous motif in membrane signaling. The solution reveals that partitioning improves signaling reliability via two effects: it moderates the non-linearity of the switching response, and it reduces noise in the response by suppressing correlations between molecules. An optimal partition size arises from a trade-off between minimizing the number of proteins per partition to improve signaling reliability and ensuring sufficient proteins per partition to maintain signal propagation. The predicted optimal partition size agrees quantitatively with experimentally observed systems. These results persist in spatial simulations with explicit diffusion barriers. Our findings suggest that molecular partitioning is not merely a consequence of the complexity of cellular substructures, but also plays an important functional role in cell signaling."]]></description>
<dc:subject>to:NB biochemical_networks biological_computation signal_transduction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:76b0287641bf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.4772">
    <title>[1302.4772] Time-dependent information transmission in a model regulatory circuit</title>
    <dc:date>2013-02-21T23:41:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.4772</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many biological regulatory systems process signals out of steady state and respond with a physiological delay. A simple model of regulation which respects these features shows how the ability of a delayed output to transmit information is limited: at short times by the timescale of the dynamic input, at long times by that of the dynamic output. We find that topologies of maximally informative networks correspond to commonly occurring biological circuits linked to stress response and that circuits functioning out of steady state may exploit absorbing states to transmit information optimally."]]></description>
<dc:subject>to:NB information_theory biological_computation kith_and_kin to_read biochemical_networks wiggins.chrisropher</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:43409390194c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiggins.chrisropher"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.4450">
    <title>[1212.4450] Minimal autocatalytic networks</title>
    <dc:date>2012-12-19T13:33:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.4450</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Self-sustaining autocatalytic chemical networks represent a necessary, though not sufficient condition for the emergence of early living systems. These networks have been formalised and investigated within the framework of RAF theory, which has led to a number of insights and results concerning the likelihood of such networks forming. In this paper, we extend this analysis by focussing on how {em small} autocatalytic networks are likely to be when they first emerge. First we show that simulations are unlikely to settle this question, by establishing that the problem of finding a smallest RAF within a catalytic reaction system is NP-hard. However, irreducible RAFs (irrRAFs) can be constructed in polynomial time, and we show it is possible to determine in polynomial time whether a bounded size set of these irrRAFs contain the smallest RAFs within a system. Moreover, we derive rigorous bounds on the sizes of small RAFs and use simulations to sample irrRAFs under the binary polymer model. We then apply mathematical arguments to prove a new result suggested by those simulations: at the transition catalysis level at which RAFs first form in this model, small RAFs are unlikely to be present. We also investigate further the relationship between RAFs and another formal approach to self-sustaining and closed chemical networks, namely chemical organisation theory (COT)."]]></description>
<dc:subject>to:NB biochemical_networks autocatalysis self-organization evolution_of_life kith_and_kin hordijk.wim</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92c1dc2b20de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autocatalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hordijk.wim"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.738614">
    <title>Taylor &amp; Francis Online :: Statistical Challenges in Biological Networks - Journal of Computational and Graphical Statistics - Volume 21, Issue 4</title>
    <dc:date>2012-12-15T15:04:16+00:00</dc:date>
    <link>http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.738614</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Advances in high-throughput technologies have made available a large amount of genomic, transcriptomic, proteomic, and metabolomic biological data. For biomedical researchers to make sense of the vast amount of information contained in such data, and incorporate structural information and knowledge gleaned from targeted experiments, networks can play a key role in their understanding of biological processes and mechanisms. This article discusses some topics pertaining to biological networks and outline the role of statistical methods in their analysis."]]></description>
<dc:subject>to:NB statistics network_data_analysis biochemical_networks bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:84abb1018253/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bioinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoas/1346418574">
    <title>Katenka , Kolaczyk : Inference and characterization of multi-attribute networks with application to computational biology</title>
    <dc:date>2012-08-31T16:33:53+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoas/1346418574</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Our work is motivated by and illustrated with application of association networks in computational biology, specifically in the context of gene/protein regulatory networks. Association networks represent systems of interacting elements, where a link between two different elements indicates a sufficient level of similarity between element attributes. While in reality relational ties between elements can be expected to be based on similarity across multiple attributes, the vast majority of work to date on association networks involves ties defined with respect to only a single attribute. We propose an approach for the inference of multi-attribute association networks from measurements on continuous attribute variables, using canonical correlation and a hypothesis-testing strategy. Within this context, we then study the impact of partial information on multi-attribute network inference and characterization, when only a subset of attributes is available. We consider in detail the case of two attributes, wherein we examine through a combination of analytical and numerical techniques the implications of the choice and number of node attributes on the ability to detect network links and, more generally, to estimate higher-level network summary statistics, such as node degree, clustering coefficients and measures of centrality. Illustration and applications throughout the paper are developed using gene and protein expression measurements on human cancer cell lines from the NCI-60 database."]]></description>
<dc:subject>network_data_analysis statistics biochemical_networks in_NB to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e39383a18e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.5532">
    <title>[1207.5532] The compositional and evolutionary logic of metabolism</title>
    <dc:date>2012-07-25T02:44:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.5532</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. 
"We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. 
"The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells."]]></description>
<dc:subject>to:NB to_read biochemical_networks kith_and_kin smith.eric</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:64f12e4f0d45/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smith.eric"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftp.stat.duke.edu/WorkingPapers/09-10.html">
    <title>Multiscale factor models for molecular networks</title>
    <dc:date>2012-07-12T20:33:58+00:00</dc:date>
    <link>http://ftp.stat.duke.edu/WorkingPapers/09-10.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A factor modeling framework is developed that is both predictive of phenotypic or response variation and the inferred factors offer insight with respect to underlying physical or biological processes. The method is general and can be applied to a variety of scientific problems. We focus on modeling complex disease phenotypes (etiology of cancer) as a motivating example. In this setting, the factors capture gene or protein interaction networks at different scales -- breadth of the interaction network. The method integrates multiscale analysis on graphs and manifolds developed in applied harmonic analysis with sparse factor models, a mainstay of applied statistics. Specific findings include the association of the TGF-$beta$ pathway with prostate cancer recurrence mediated by cell-cycle control and the implication of the p27 pathway in cancer progression. In silico perturbation analyses of the inferred multiscale model suggest that the TGF-$beta$ pathway is a dominant pathway in control of cell-cycle deregulation in prostate cancer."]]></description>
<dc:subject>to:NB factor_analysis gene_expression_data_analysis biochemical_networks statistics network_data_analysis mukherjee.sayan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ac872a942148/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mukherjee.sayan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&amp;tid=12757">
    <title>Ingenious Genes: How Gene Regulation Networks Evolve to Control Ontogeny - The MIT Press</title>
    <dc:date>2012-07-06T14:38:34+00:00</dc:date>
    <link>http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&amp;tid=12757</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Each of us is a collection of more than ten trillion cells, busy performing tasks crucial to our continued existence. Gene regulation networks, consisting of a subset of genes called transcription factors, control cellular activity--producing the right gene activities for the many situations that the multiplicity of cells in our bodies face. Genes working together make up a truly ingenious system. In this book, Roger Sansom investigates how gene regulation works and how such a refined but simple system evolved.
"Sansom describes in detail two frameworks for understanding gene regulation. The first, developed by the theoretical biologist Stuart Kauffman, holds that gene regulation networks are fundamentally systems that repeat patterns of gene expression. Sansom finds Kauffman's framework an inadequate explanation for how cells overcome the difficulty of development. Sansom proposes an alternative: the connectionist framework. Drawing on work from artificial intelligence and philosophy of mind, he argues that the key lies in how multiple transcription factors combine to regulate a single gene, acting in a way that is qualitatively consistent. This allows the expression of genes to be finely tuned to the variable microenvironments of cells. Because of the nature of both development and its evolution, we can gain insight into the developmental process when we identify gene regulation networks as the controllers of development. The ingenuity of genes is explained by how gene regulation networks evolve to control development. "]]></description>
<dc:subject>to:NB books:noted gene_regulation biochemical_networks evolutionary_biology evo-devo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:998a260e88dd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evo-devo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nature/journal/v487/n7405/full/nature11149.html">
    <title>Programmable single-cell mammalian biocomputers : Nature : Nature Publishing Group</title>
    <dc:date>2012-07-05T14:19:46+00:00</dc:date>
    <link>http://www.nature.com/nature/journal/v487/n7405/full/nature11149.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Synthetic biology is producing genetic circuits of increasing complexity, most of them based on networks derived from microorganisms. Martin Fussenegger and colleagues have turned to mammalian control devices as a basis for the design of a variety of fundamental logic gates. They combine regulatory proteins that bind either DNA or RNA — to control gene transcription or translation, respectively — to reprogram mammalian cellular functions. Through a 'plug-and-play' combination of their basic circuits, they build a variety of computational logic gates (NOT, AND, NAND and N-IMPLY), which could pave the way for precise and robust control of future gene- and cell-based therapies."]]></description>
<dc:subject>to:NB biochemical_networks molecular_biology experimental_biology did_someone_say_blood_music? biological_computers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b60d5a9d2002/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:molecular_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:did_someone_say_blood_music?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jneurosci.org/content/32/25/8732.short?rss=1">
    <title>Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in 472 Twins</title>
    <dc:date>2012-06-23T14:45:44+00:00</dc:date>
    <link>http://www.jneurosci.org/content/32/25/8732.short?rss=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 ± 2.1 SD years; 193 male/279 female). We combined clustering with genome-wide scanning to find brain systems with common genetic determination. We then filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus."]]></description>
<dc:subject>to:NB neuroscience biochemical_networks human_genetics network_data_analysis causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da24282a2130/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0801.0254">
    <title>[0801.0254] Design of experiments and biochemical network inference</title>
    <dc:date>2012-06-17T21:25:09+00:00</dc:date>
    <link>http://arxiv.org/abs/0801.0254</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Design of experiments is a branch of statistics that aims to identify efficient procedures for planning experiments in order to optimize knowledge discovery. Network inference is a subfield of systems biology devoted to the identification of biochemical networks from experimental data. Common to both areas of research is their focus on the maximization of information gathered from experimentation. The goal of this paper is to establish a connection between these two areas coming from the common use of polynomial models and techniques from computational algebra."]]></description>
<dc:subject>to:NB biochemical_networks network_data_analysis gene_expression_data_analysis algebraic_statistics statistics experimental_design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:19ffc658e3ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebraic_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/knowledge/isbn/item2327615/?site_locale=en_US">
    <title>Protein Interaction Networks - Academic and Professional Books - Cambridge University Press</title>
    <dc:date>2012-04-23T13:59:02+00:00</dc:date>
    <link>http://www.cambridge.org/us/knowledge/isbn/item2327615/?site_locale=en_US</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, processes, and functions. Recent large-scale investigations of protein-protein interactions using such techniques as two-hybrid systems, mass spectrometry, and protein microarrays have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. The resulting large volume of protein-protein interaction data has posed a challenge to experimental investigation. This book provides a comprehensive understanding of the computational methods available for the analysis of protein-protein interaction networks. It offers an in-depth survey of a range of approaches, including statistical, topological, data-mining, and ontology-based methods. The author discusses the fundamental principles underlying each of these approaches and their respective benefits and drawbacks, and she offers suggestions for future research."]]></description>
<dc:subject>to:NB books:noted biochemical_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e6684dbe06fc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0704.2551">
    <title>[0704.2551] Inferring dynamic genetic networks with low order independencies</title>
    <dc:date>2012-03-18T18:53:18+00:00</dc:date>
    <link>http://arxiv.org/abs/0704.2551</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we propose a novel inference method for dynamic genetic networks which makes it possible to face with a number of time measurements n much smaller than the number of genes p. The approach is based on the concept of low order conditional dependence graph that we extend here in the case of Dynamic Bayesian Networks. Most of our results are based on the theory of graphical models associated with the Directed Acyclic Graphs (DAGs). In this way, we define a minimal DAG G which describes exactly the full order conditional dependencies given the past of the process. Then, to face with the large p and small n estimation case, we propose to approximate DAG G by considering low order conditional independencies. We introduce partial qth order conditional dependence DAGs G(q) and analyze their probabilistic properties. In general, DAGs G(q) differ from DAG G but still reflect relevant dependence facts for sparse networks such as genetic networks. By using this approximation, we set out a non-bayesian inference method and demonstrate the effectiveness of this approach on both simulated and real data analysis. The inference procedure is implemented in the R package 'G1DBN' freely available from the CRAN archive."]]></description>
<dc:subject>to:NB graphical_models gene_expression_data_analysis biochemical_networks statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2363f69da42b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_expression_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencemag.org/content/335/6072/1099.abstract">
    <title>Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism</title>
    <dc:date>2012-03-03T16:29:22+00:00</dc:date>
    <link>http://www.sciencemag.org/content/335/6072/1099.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs."]]></description>
<dc:subject>to:NB to_read biochemical_networks adaptive_behavior experimental_biology re:network_differences gene_regulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a0d1534d2e13/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:adaptive_behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:network_differences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0811.2834">
    <title>[0811.2834] Quantifying evolvability in small biological networks</title>
    <dc:date>2012-01-23T19:04:38+00:00</dc:date>
    <link>http://arxiv.org/abs/0811.2834</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks."]]></description>
<dc:subject>to:NB evolutionary_biology biochemical_networks kith_and_kin wiggins.christopher</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d713e806e649/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiggins.christopher"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0811.4149">
    <title>[0811.4149] A stochastic spectral analysis of transcriptional regulatory cascades</title>
    <dc:date>2012-01-01T15:42:20+00:00</dc:date>
    <link>http://arxiv.org/abs/0811.4149</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The past decade has seen great advances in our understanding of the role of noise in gene regulation and the physical limits to signaling in biological networks. Here we introduce the spectral method for computation of the joint probability distribution over all species in a biological network. The spectral method exploits the natural eigenfunctions of the master equation of birth-death processes to solve for the joint distribution of modules within the network, which then inform each other and facilitate calculation of the entire joint distribution. We illustrate the method on a ubiquitous case in nature: linear regulatory cascades. The efficiency of the method makes possible numerical optimization of the input and regulatory parameters, revealing design properties of, e.g., the most informative cascades. We find, for threshold regulation, that a cascade of strong regulations converts a unimodal input to a bimodal output, that multimodal inputs are no more informative than bimodal inputs, and that a chain of up-regulations outperforms a chain of down-regulations. We anticipate that this numerical approach may be useful for modeling noise in a variety of small network topologies in biology."]]></description>
<dc:subject>to:NB biochemical_networks kith_and_kin wiggins.christopher</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30629d2f1d09/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiggins.christopher"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.1047">
    <title>[1112.1047] Network Inference and Biological Dynamics</title>
    <dc:date>2011-12-12T16:18:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.1047</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a general formulation, we also explore the relationship between single-cell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners and suggestions for experimental design."

ETA: published as  http://projecteuclid.org/euclid.aoas/1346418580 .]]></description>
<dc:subject>have_read biochemical_networks network_data_analysis in_NB statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fd258d20535d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pre.aps.org/abstract/PRE/v84/i5/e051917">
    <title>Phys. Rev. E 84, 051917 (2011): Nonequilibrium phase transitions in biomolecular signal transduction</title>
    <dc:date>2011-11-26T16:14:38+00:00</dc:date>
    <link>http://pre.aps.org/abstract/PRE/v84/i5/e051917</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a mechanism for reliable switching in biomolecular signal-transduction cascades. Steady bistable states are created by system-size cooperative effects in populations of proteins, in spite of the fact that the phosphorylation-state transitions of any molecule, by means of which the switch is implemented, are highly stochastic. The emergence of switching is a nonequilibrium phase transition in an energetically driven, dissipative system described by a master equation. We use operator and functional integral methods from reaction-diffusion theory to solve for the phase structure, noise spectrum, and escape trajectories and first-passage times of a class of minimal models of switches, showing how all critical properties for switch behavior can be computed within a unified framework."]]></description>
<dc:subject>to:NB heard_the_talk kith_and_kin signal_transduction biochemical_networks phase_transitions statistical_mechanics non-equilibrium smith.eric fontana.walter krakauer.david</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5310b5a0ae5f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smith.eric"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fontana.walter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krakauer.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0812.2184">
    <title>[0812.2184] Protein-Interaction-Networks: More than mere modules</title>
    <dc:date>2011-10-12T15:43:33+00:00</dc:date>
    <link>http://arxiv.org/abs/0812.2184</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cellular function is widely believed to be organized in a modular fashion. On all scales and at all levels of complexity, relatively independent sub-units perform relatively independent sub-tasks of biological function. This functional modularity must be reflected in the topology of molecular networks. But how a functional module should be represented in an interaction network is an open question. In protein-interaction networks (PIN), one can identify a protein-complex as a module on a small scale, i.e. modules are understood as densely linked, resp. interacting, groups of proteins, that are only sparsely interacting with the rest of the network. 
In this contribution, we show that extrapolating this concept of cohesively linked clusters of proteins as modules to the scale of the entire PIN inevitable misses important and functionally relevant structure inherent in the network. As an alternative, we introduce a novel way of decomposing a network into functional roles and show that this represents network structure and function more efficiently. This finding should have a profound impact on all module assisted methods of protein function prediction and should shed new light on how functional modules can be represented in molecular interaction networks in general."]]></description>
<dc:subject>community_discovery biochemical_networks reichardt.joerg in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d6cd8fc3b868/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reichardt.joerg"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1101.4240">
    <title>[1101.4240] Information transmission in genetic regulatory networks: a review</title>
    <dc:date>2011-02-04T07:31:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1101.4240</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>signal_transduction gene_regulation biochemical_networks information_theory biological_computation in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10aa75576a76/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:signal_transduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gene_regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biological_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1012.1473">
    <title>[1012.1473] Randomizing genome-scale metabolic networks</title>
    <dc:date>2010-12-13T22:12:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1012.1473</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A network observed in a particular context may appear to have "unusual" properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the straightforward randomization of the network generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles for randomizing such metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations and show how they allow one to approach the properties of biological metabolic networks. An implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints."
]]></description>
<dc:subject>network_data_analysis biochemical_networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e217732405e5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/9780521886086">
    <title>Statistical Mechanics of Cellular Systems and Processes - Academic and Professional Books - Cambridge University Press</title>
    <dc:date>2010-10-15T11:54:59+00:00</dc:date>
    <link>http://www.cambridge.org/9780521886086</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cells are complex objects, representing a multitude of structures and processes. In order to understand the organization, interaction and hierarchy of these structures and processes, a quantitative understanding is absolutely critical. Traditionally, statistical mechanics-based treatment of biological systems has focused on the molecular level, with larger systems being ignored. This book integrates understanding from the molecular to the cellular and multi-cellular level in a quantitative framework that will benefit a wide audience engaged in biological, biochemical, biophysical and clinical research. It will build new bridges of quantitative understanding that link fundamental physical principles governing cellular structure and function with implications in clinical and biomedical contexts."
]]></description>
<dc:subject>books:noted statistical_mechanics biology biochemical_networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:351be5957314/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/artl_a_00001">
    <title>Concurrency and Network Disassortativity</title>
    <dc:date>2010-09-01T15:03:51+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/artl_a_00001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The relationship between a network's degree-degree correlation and a loose version of graph coloring is studied on networks with broad degree distributions. We find that, given similar conditions on the number of nodes, number of links, and clustering levels, fewer colors are needed to color disassortative than assortative networks. Since fewer colors create fewer independent sets, our finding implies that disassortative networks may have higher concurrency potential than assortative networks. This in turn suggests another reason for the disassortative mixing pattern observed in biological networks such as those of protein-protein interaction and gene regulation. In addition to the functional specificity and stability suggested by Maslov and Sneppen, a disassortative network topology may also enhance the ability of cells to perform crucial tasks concurrently..." Related to Judd/Kearns experiments on human graph coloring?
]]></description>
<dc:subject>graph_theory networks biochemical_networks</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7f5274347994/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>