<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://www.nature.com/articles/s41557-025-01981-y"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41586-024-08370-4"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2312.13944"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/10.1101/2020.07.31.230334v1?rss=1"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/10.1101/130112v1?rss=1"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/10.1101/863159v2"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1811.05371"/>
	<rdf:li rdf:resource="http://htsang.wikidot.com/research"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1806.10764"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1711.08988"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1308.6619"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1605.06149#"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1605.01624"/>
	<rdf:li rdf:resource="http://biorxiv.org/content/early/2016/03/18/044628?rss=1%2522"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1501.03971"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1501.04040"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1405.5621"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1508.01737"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.3138"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1410.4465"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1403.0336"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1410.4530"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1410.0652"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1309.0765"/>
	<rdf:li rdf:resource="http://www.copasi.org/tiki-read_article.php?articleId=55"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.6209"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.0688"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.4146"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1310.7899"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1310.8267"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1305.3007"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1306.0069"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1207.6431"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1304.7241"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1212.4312"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1211.0662"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1207.3453"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.1571"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1105.2204"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.1098"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1010.4735"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1109.2618"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1112.5794"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1109.5389"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1105.4335"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1007.2668"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1003.2791"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.4265"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1002.4273"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.1142"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.1191"/>
	<rdf:li rdf:resource="http://newscenter.lbl.gov/feature-stories/2010/05/10/untangling-quantum-entanglement/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0712.4248"/>
      </rdf:Seq>
    </items>
  </channel><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>2025-12-10T14:31:54+00:00</dc:date>
    <link>https://www.nature.com/articles/s41557-025-01981-y</link>
    <dc:creator>Vaguery</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>reaction-networks artificial-life reservoir-computing biochemistry nonlinear-dynamics indistinguishable-from-magic to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ed6e937bb4ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reservoir-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:indistinguishable-from-magic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-024-08370-4">
    <title>Site-saturation mutagenesis of 500 human protein domains | Nature</title>
    <dc:date>2025-04-12T13:22:20+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-024-08370-4</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1. Tens of millions of missense variants exist in the current human population, and the vast majority of these have unknown functional consequences. Here we present a large-scale experimental analysis of human missense variants across many different proteins. Using DNA synthesis and cellular selection experiments we quantify the effect of more than 500,000 variants on the abundance of more than 500 human protein domains. This dataset reveals that 60% of pathogenic missense variants reduce protein stability. The contribution of stability to protein fitness varies across proteins and diseases and is particularly important in recessive disorders. We combine stability measurements with protein language models to annotate functional sites across proteins. Mutational effects on stability are largely conserved in homologous domains, enabling accurate stability prediction across entire protein families using energy models. Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.

]]></description>
<dc:subject>structural-biology biochemistry molecular-biology impressive looking-to-see neural-networks protein-folding to-write-about evolutionary-biology variation-as-a-source-of-innovation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:604c7c235a9a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:impressive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:variation-as-a-source-of-innovation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2312.13944">
    <title>[2312.13944] Docking-based generative approaches in the search for new drug candidates</title>
    <dc:date>2024-07-03T10:48:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2312.13944</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Despite the great popularity of virtual screening of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design. In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design. In addition, we discuss the most promising directions for further development of generative protocols coupled with docking.
]]></description>
<dc:subject>biochemistry pharmaceutical design-automation rather-interesting simulation molecular-design molecular-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9f055f81cf4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pharmaceutical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design-automation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/2020.07.31.230334v1?rss=1">
    <title>MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics | bioRxiv</title>
    <dc:date>2021-03-26T20:19:28+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2020.07.31.230334v1?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner; the Systems Biology Markup Language (SBML) simulation engine. Three case studies demonstrate how to use MASSpy: 1) to simulate dynamics of detailed mechanisms of enzyme regulation; 2) to generate an ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify uncertainty, and 3) to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenge that arise in dynamic modeling of metabolic networks, both at a small and large scale.

]]></description>
<dc:subject>python systems-biology theoretical-biology rather-interesting biochemistry models visualization project-jupyter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:134d6e5931f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:project-jupyter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/130112v1?rss=1">
    <title>Optimal information transfer in enzymatic networks: A field theoretic formulation | bioRxiv</title>
    <dc:date>2020-07-09T23:31:28+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/130112v1?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach in order to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus (Phys. Rev. X., 4, 041017 (2014)). We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudo intermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudo intermediate. Surprisingly, in these examples the minimum error computed using simulations that take non-linearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in networks of arbitrary complexity.

]]></description>
<dc:subject>systems-biology information-theory molecular-machinery rather-interesting cell-biology biochemistry reaction-networks to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8b54d2690dab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/863159v2">
    <title>Multiplexing information flow through dynamic signalling systems | bioRxiv</title>
    <dc:date>2020-06-17T14:01:31+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/863159v2</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-κB transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-κB that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback–Leibler divergences and sensitivity matrices, and link these methods to new theory about multiplexing information. We show that many current models such as those of the NF-κB system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.

]]></description>
<dc:subject>systems-biology reaction-networks biological-engineering biological-design theoretical-biology biochemistry to-write-about to-simulate consider:search-space consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:36b26660a270/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:search-space"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.05371">
    <title>[1811.05371] The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks</title>
    <dc:date>2020-06-14T12:00:52+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.05371</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Eukaryotic cells transmit information by signaling through complex networks of interacting proteins. Here we develop a theoretical and computational framework that relates the biophysics of protein-protein interactions (PPIs) within a signaling network to its information processing properties. To do so, we generalize statistical physics-inspired models for protein binding to account for interactions that depend on post-translational state (e.g. phosphorylation). By combining these models with information-theoretic methods, we find that PPIs are a key determinant of information transmission within a signaling network, with weak interactions giving rise to "noise" that diminishes information transmission. While noise can be mitigated by increasing interaction strength, the accompanying increase in transmission comes at the expense of a slower dynamical response. This suggests that the biophysics of signaling protein interactions give rise to a fundamental "speed-information" trade-off. Surprisingly, we find that cross-talk between pathways in complex signaling networks do not significantly alter information capacity--an observation that may partially explain the promiscuity and ubiquity of weak PPIs in heavily interconnected networks. We conclude by showing how our framework can be used to design synthetic biochemical networks that maximize information transmission, a procedure we dub "InfoMax" design.
]]></description>
<dc:subject>theoretical-biology systems-biology biochemistry reaction-networks rather-interesting to-simulate to-write-about consider:abstractions consider:interactive-visualization biological-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:652e1aef1dee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:abstractions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:interactive-visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://htsang.wikidot.com/research">
    <title>DR. HERBERT H. TSANG - http://www.herberttsang.org</title>
    <dc:date>2020-02-09T01:14:49+00:00</dc:date>
    <link>http://htsang.wikidot.com/research</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[RNA design algorithm takes an RNA secondary structure description as input and then try to identify an RNA strand that folds into this function-specific target structure. With new advances in biotechnology and synthetic biology, a reliable RNA design algorithm can be crucial steps to create new biochemical components. Our lab is interested in employing various computational intelligence techniques to propose the new paradigm to help with the RNA design problem. Recently, we have designed an algorithm SIMARD, which is based on the simulated annealing paradigm.
]]></description>
<dc:subject>structural-biology polymer-folding biochemistry biophysics simulation metaheuristics energy-landscapes rather-interesting to-write-about to-simulate to-visualize</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d531cf9636cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:polymer-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-visualize"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1806.10764">
    <title>[1806.10764] Biochemical Coupling Through Emergent Conservation Laws</title>
    <dc:date>2019-09-29T10:38:06+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.10764</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where fast reactions have reached an approximate steady state while slow reactions have not yet reached equilibrium. After an expository introduction to the relevant aspects of reaction network theory, we review his work and explain the role of emergent conserved quantities in coupling. These are quantities, left unchanged by fast reactions, whose conservation forces exergonic processes such as ATP hydrolysis to drive desired endergonic processes.
]]></description>
<dc:subject>reaction-networks thermodynamics category-theory hypergraphs emergent-design rather-interesting to-understand biochemistry thesis-like</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d417616afa87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:category-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hypergraphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thesis-like"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.08988">
    <title>[1711.08988] Exponential growth for self-reproduction in a catalytic reaction network: relevance of a minority molecular species and crowdedness</title>
    <dc:date>2018-02-22T00:18:19+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.08988</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary to acquire evolvability at the primitive stage of life.]]></description>
<dc:subject>autocatalysis artificial-life origin-of-life reaction-networks self-organization biochemistry simulation rather-interesting to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:58e22fb5fef7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autocatalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1308.6619">
    <title>[1308.6619] Backward stochastic differential equation approach to modeling of gene expression</title>
    <dc:date>2017-03-05T21:57:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1308.6619</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this article, we introduce a novel backward method to model stochastic gene expression and protein level dynamics. The protein amount is regarded as a diffusion process and is described by a backward stochastic differential equation (BSDE). Unlike many other SDE techniques proposed in the literature, the BSDE method is backward in time; that is, instead of initial conditions it requires the specification of endpoint ("final") conditions, in addition to the model parametrization. To validate our approach we employ Gillespie's stochastic simulation algorithm (SSA) to generate (forward) benchmark data, according to predefined gene network models. Numerical simulations show that the BSDE method is able to correctly infer the protein level distributions that preceded a known final condition, obtained originally from the forward SSA. This makes the BSDE method a powerful systems biology tool for time reversed simulations, allowing, for example, the assessment of the biological conditions (e.g. protein concentrations) that preceded an experimentally measured event of interest (e.g. mitosis, apoptosis, etc.).
]]></description>
<dc:subject>stochastic-systems biochemistry systems-biology simulation rather-interesting cheminformatics gene-regulatory-networks to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4532ddd362ef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cheminformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gene-regulatory-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1605.06149#">
    <title>[1605.06149] How can the green sulfur bacteria use quantum computing for light harvesting?</title>
    <dc:date>2016-07-04T00:32:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.06149#</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Long lasting coherence in photosynthetic pigment-protein complexes has been observed even at physiological temperatures. Experiments have demonstrated quantum coherent behaviour in the long-time operation of the D-Wave quantum computer as well. Quantum coherence is the common feature between the two phenomena. An explanations for eight orders of magnitude discrepancy between the single flux qubit coherence time and the long-time quantum behaviour of an array of thousand flux qubits in the quantum computer was suggested within a theory where the flux qubits are coupled to an environment of particles called gravonons of high density of states The coherent evolution is in high dimensional spacetime and can be understood as a solution of Schroedinger's time-dependent equation. 
Explanations for the quantum beats observed in 2D Fourier transform electronic spectroscopy of the Fenna-Matthews-Olson (FMO) protein complex in the green sulfur bacteria are presently sought in constructing transport theories based on quantum master equations where 'good' molecular vibrations ('coloured noise') in the chlorophyll and the surrounding protein scaffold knock the exciton oscillations back into coherence. These 'good' vibrations are claimed to have developed in three billion years of natural selection. These theories, however, face the discomforting experimental observation that "attempts to scramble vibrational modes or to shift resonances with isotopic substitution miserably failed to affect the beating signals". As a possible way out of this dilemma we adopted the formalism of the quantum computation to the quantum beats in the FMO protein complex.
]]></description>
<dc:subject>rather-interesting quantums quantum-computing systems-biology biochemistry to-understand to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:644d88941f31/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantums"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantum-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</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>2016-05-09T11:47:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.01624</link>
    <dc:creator>Vaguery</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>biochemistry systems-biology S-systems nonlinear-dynamics biological-engineering nudge-targets consider:feature-discovery consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bbe015942a29/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:S-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biorxiv.org/content/early/2016/03/18/044628?rss=1%2522">
    <title>A universal mathematical model of non-photochemical quenching to study short-term light memory in plants | bioRxiv</title>
    <dc:date>2016-03-26T22:10:31+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2016/03/18/044628?rss=1%2522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The concept of plant memory triggers a quite controversial dispute on the definition, extent or even existence of such. Because plants are permanently exposed to rapidly changing environments it is evident that they had to evolve mechanisms enabling them to dynamically adapt to such fluctuations. Recognizing memory as a timed response to changes of external inputs through amplification and integration of multiple signals, here we study the short-term illumination memory in Arabidopsis thaliana by monitoring fluorescence emission dynamics. For this, we designed an experiment to systematically determine the extent of non-photochemical quenching (NPQ) after previous light exposure. We propose a simplified, mathematical model of photosynthesis that includes the key components required for NPQ activation. Due to its reduced complexity, our model is universally applicable to other species, which we demonstrate by adapting it to the shadow-tolerant plant Epipremnum aureum. We demonstrate that a basic mechanism of short-term light memory, which is based on two interacting components, can explain our experimental observations. The slow component, accumulation of zeaxanthin, accounts for the amount of memory remaining after relaxation in darkness, while the fast one, antennae protonation, increases quenching efficiency. With this combined theoretical and experimental approach we provide a unifying framework that helps to uncover general principles of key photoprotective mechanisms across species.

]]></description>
<dc:subject>systems-biology biochemistry photobiology theoretical-biology dynamical-systems biological-engineering rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:62bf291251d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:photobiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.03971">
    <title>[1501.03971] Bayesian protein structure alignment</title>
    <dc:date>2016-02-27T21:43:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.03971</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The analysis of the three-dimensional structure of proteins is an important topic in molecular biochemistry. Structure plays a critical role in defining the function of proteins and is more strongly conserved than amino acid sequence over evolutionary timescales. A key challenge is the identification and evaluation of structural similarity between proteins; such analysis can aid in understanding the role of newly discovered proteins and help elucidate evolutionary relationships between organisms. Computational biologists have developed many clever algorithmic techniques for comparing protein structures, however, all are based on heuristic optimization criteria, making statistical interpretation somewhat difficult. Here we present a fully probabilistic framework for pairwise structural alignment of proteins. Our approach has several advantages, including the ability to capture alignment uncertainty and to estimate key "gap" parameters which critically affect the quality of the alignment. We show that several existing alignment methods arise as maximum a posteriori estimates under specific choices of prior distributions and error models. Our probabilistic framework is also easily extended to incorporate additional information, which we demonstrate by including primary sequence information to generate simultaneous sequence-structure alignments that can resolve ambiguities obtained using structure alone. This combined model also provides a natural approach for the difficult task of estimating evolutionary distance based on structural alignments. The model is illustrated by comparison with well-established methods on several challenging protein alignment examples.
]]></description>
<dc:subject>biochemistry structural-biology protein-folding bioinformatics algorithms machine-learning statistics metrics nudge-targets consider:distance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:eefeff02cf1a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:distance-measure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.04040">
    <title>[1501.04040] Nonlinearity arising from noncooperative transcription factor binding enhances negative feedback and promotes genetic oscillations</title>
    <dc:date>2015-12-15T15:08:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.04040</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the effects of multiple binding sites in the promoter of a genetic oscillator. We evaluate the regulatory function of a promoter with multiple binding sites in the absence of cooperative binding, and consider different hypotheses for how the number of bound repressors affects transcription rate. Effective Hill exponents of the resulting regulatory functions reveal an increase in the nonlinearity of the feedback with the number of binding sites. We identify optimal configurations that maximize the nonlinearity of the feedback. We use a generic model of a biochemical oscillator to show that this increased nonlinearity is reflected in enhanced oscillations, with larger amplitudes over wider oscillatory ranges. Although the study is motivated by genetic oscillations in the zebrafish segmentation clock, our findings may reveal a general principle for gene regulation.
]]></description>
<dc:subject>systems-biology biophysics biochemistry reaction-networks engineering-design biological-engineering nudge-targets nonlinear-dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1d64e177a2d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1405.5621">
    <title>[1405.5621] A Theory of Decomposition of Complex Chemical Networks using the Hill Functions</title>
    <dc:date>2015-12-11T22:27:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1405.5621</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The design and synthesis of complex and large mimicked biochemical networks de novo is an unsolved problem in synthetic biology. To address this limitation without resorting to ad hoc computations and experiments, a predictive mathematical theory is required to reduce these complex chemical networks into natural physico-chemical expressions. Here we provide a theory that offers a physico-chemical expression for a large chemical network that is almost arbitrarily both nonlinear and complex. Unexpectedly, the theory demonstrates that such networks can be decomposed into reactions based solely on the Hill equation, a simple chemical logic gate. This theory, analogous to implemented electrical logic gates or functional algorithms in a computer, is proposed for implementing regulated sequences of functional chemical reactions, such as mimicked genes, transcriptional regulation, signal transduction, protein interaction, and metabolic networks, into an artificial designed chemical network.
]]></description>
<dc:subject>theoretical-biology systems-biology simulation generative-models rather-interesting biochemistry reaction-networks nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2e0c3fd40f36/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1508.01737">
    <title>[1508.01737] Comparison of moment-closure approximations for stochastic chemical kinetics</title>
    <dc:date>2015-09-04T19:11:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1508.01737</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In recent years moment-closure approximations (MA) of the chemical master equation have become a popular method for the study of stochastic effects in chemical reaction systems. Several different MA methods have been proposed and applied in the literature, but it remains unclear how they perform with respect to each other. In this paper we study the normal, Poisson, log-normal and central-moment-neglect MAs by applying them to understand the stochastic properties of chemical systems whose deterministic rate equations show the properties of bistability, ultrasensitivity and oscillatory behaviour. Our results suggest that the normal MA is favourable over the other studied MAs. In particular we found that (i) the size of the region of parameter space where a closure gives physically meaningful results, e.g. positive mean and variance, is considerably larger for the normal closure than for the other three closures; (ii) the accuracy of the predictions of the four closures (relative to simulations using the stochastic simulation algorithm) is comparable in those regions of parameter space where all closures give physically meaningful results; (iii) the Poisson and log-normal MAs are not uniquely defined for systems involving conservation laws in molecule numbers. We also describe the new software package MOCA which enables the automated numerical analysis of various MA methods in a graphical user interface and which was used to perform the comparative analysis presented in this paper. MOCA allows the user to develop novel closure methods and can treat polynomial, non-polynomial, as well as time-dependent propensity functions, thus being applicable to virtually any chemical reaction system.
]]></description>
<dc:subject>reaction-networks systems-biology biochemistry numerical-methods robustness approximation stochastic-systems models horse-races performance-measure nudge-targets consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:200bf1fc5411/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.3138">
    <title>[1412.3138] Computational Protein Design Using AND/OR Branch-and-Bound Search</title>
    <dc:date>2015-07-25T11:29:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.3138</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many prob- lems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.
]]></description>
<dc:subject>structural-biology biochemistry constraint-satisfaction biological-engineering engineering-design nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fcb821ffbd3e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.4465">
    <title>[1410.4465] On the abundance of intrinsically disordered proteins in the human proteome and its relation to diseases: there is no enrichment</title>
    <dc:date>2015-03-15T20:39:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.4465</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Intrinsically disordered proteins are fascinating the community of protein science since the last decade, at least. There is a well-established line of research that intends to reveal the crucial role played by intrinsically disordered proteins (IDPs) in the development of human diseases. The main argument is that IDPs are differentially more present in groups of disease-related proteins. In this note we compare the frequency of disorder in human proteins, both disease-related and not. The frequency of disorder is comparable in the two sub-groups of proteins. Disorder is widespread in human proteins, but it is not a specific pre-requisite of proteins involved in the development of cancer, cardiovascular diseases, diabetes and neurodegenerative diseases. A tendency of cancer-related proteins to be statistically more disordered than the rest of human proteins is confirmed.
]]></description>
<dc:subject>bioinformatics biochemistry structural-biology protein-folding Thom-LaBean's-thesis it's-more-complicated-than-you-think Oh Science.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e941d1fa5934/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Thom-LaBean's-thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Oh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Science."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.0336">
    <title>[1403.0336] States with identical steady dissipation rate: Role of kinetic constants in enzyme catalysis</title>
    <dc:date>2014-12-24T12:40:22+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.0336</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A non-equilibrium steady state is characterized by a non-zero steady dissipation rate. Chemical reaction systems under suitable conditions may generate such states. We propose here a method that is able to distinguish states with identical values of the steady dissipation rate. This necessitates a study of the variation of the entropy production rate with the experimentally observable reaction rate in regions close to the steady states. As an exactly-solvable test case, we choose the problem of enzyme catalysis. Link of the total entropy production with the enzyme efficiency is also established, offering a desirable connection with the inherent irreversibility of the process. The chief outcomes are finally noted in a more general reaction network with numerical demonstrations.
]]></description>
<dc:subject>reaction-networks thermodynamics biochemistry modeling nudge-targets consider:structure:elaboration performance-measure constraints</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3531b0026c7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:structure:elaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraints"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.4530">
    <title>[1410.4530] Fatigueless response of spider draglines in cyclic torsion facilitated by reversible molecular deformation</title>
    <dc:date>2014-12-06T17:13:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.4530</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate that spider draglines exhibit a fatigueless response in extreme cyclic torsion up to its breaking limit. The well defined Raman bands at 1095 and 1245cm−1 shifted linearly towards lower wavenumbers versus increasing twist in both clockwise and counter-clockwise directions. Under thousands of continuous loading cycles of twist strain approaching its breaking limit, all the Raman bands were preserved and the characteristic Raman peak shifts were found to be reversible. Besides, nanoscale surface profile of the worked silk appeared as good as the pristine silk. This unique fatigueless twist response of draglines, facilitated by reversible deformation of protein molecules, could find applications in durable miniatured devices.
]]></description>
<dc:subject>materials-science biological-engineering spiders-rock biochemistry structural-biology mechanical-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:87ddee18247e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:spiders-rock"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanical-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.0652">
    <title>[1410.0652] Using Simulations and kinetic network models to reveal the dynamics and functions of Riboswitches</title>
    <dc:date>2014-10-08T20:32:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.0652</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Riboswitches, RNA elements found in the untranslated region, regulate gene expression by binding to target metaboloites with exquisite specificity. Binding of metabolites to the conserved aptamer domain allosterically alters the conformation in the downstream expression platform. The fate of gene expression is determined by the changes in the downstream RNA sequence. As the metabolite-dependent cotranscriptional folding and unfolding dynamics of riboswitches is the key determinant of gene expression, it is important to investigate both the thermodynamics and kinetics of riboswitches both in the presence and absence of metabolite. Single molecule force experiments that decipher the free energy landscape of riboswitches from their mechanical responses, theoretical and computational studies have recently shed light on the distinct mechanism of folding dynamics in different classes of riboswitches. Here we first discuss the dynamics of water around riboswitch, highlighting that water dynamics can enhance the fluctuation of nucleic acid structure. To go beyond native state fluctuations we used the Self-Organized Polymer (SOP) model to predict the dynamics of add adenine riboswitch under mechanical forces. In addition to quantitatively predicting the folding landscape of add-riboswitch our simulations also explain the difference in the dynamics between pbuE adenine- and add adenine-riboswitches. In order to probe the function {\it in vivo} we use the folding landscape to propose a system level kinetic network model to quantitatively predict how gene expression is regulated for riboswitches that are under kinetic control.
]]></description>
<dc:subject>molecular-machinery molecular-biology biochemistry macromolecules biological-engineering nanotechnology experiment simulation nudge-targets consider:rule-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c6444c89d292/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:macromolecules"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rule-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.0765">
    <title>[1309.0765] A Model of Gene Expression Based on Random Dynamical Systems Reveals Modularity Properties of Gene Regulatory Networks</title>
    <dc:date>2014-08-22T12:38:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.0765</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) the time evolution can be easily discretized, rendering the dynamics suitable for computer simulation in a simple and direct way. Finally, it is shown that one obtains the classical rate equations form the corresponding stochastic versions as the equations satisfied by the mean values of the random variables.
]]></description>
<dc:subject>biochemistry network-theory dynamical-systems biological-engineering complexology simulation analytics nudge-targets Kauffmania engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4c8bbe60ba1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Kauffmania"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.copasi.org/tiki-read_article.php?articleId=55">
    <title>COPASI : COPASI 4.12 (Build 81) Released</title>
    <dc:date>2014-04-18T20:17:52+00:00</dc:date>
    <link>http://www.copasi.org/tiki-read_article.php?articleId=55</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>simulation biochemistry reaction-networks nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:60fa6513164e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.6209">
    <title>[1312.6209] What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems</title>
    <dc:date>2014-04-07T11:27:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.6209</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multistable gene regulatory systems sustain different levels of gene expression under identical external conditions. Such multistability is used to encode phenotypic states in processes ranging from nutrient uptake and persistence in bacteria to cell cycle control and development. Stochastic switching between different phenotypes can occur as the result of random fluctuations in molecular copy numbers of mRNA and proteins arising in transcription, translation, transport, and binding. However, which component of a pathway triggers such a transition is generally not known. By linking single-cell experiments on the lactose-uptake pathway in E. coli to molecular simulations, we devise a general method to pinpoint the particular fluctuation driving phenotype switching and apply it to the transition between the uninduced and induced states of the lac-genes. We find that the transition to the induced state is not caused only by the single event of lac-repressor unbinding, but depends crucially on the time period over which the repressor remains unbound from the lac-operon. We confirm this notion in strains with a high expression level of the repressor lacI (leading to shorter periods over which the lac-operon remains unbound), which show a reduced transition rate. Our techniques apply to multistable gene regulatory systems in general and allow to identify the molecular mechanisms behind stochastic transitions in gene regulatory circuits.
]]></description>
<dc:subject>systems-biology gene-regulatory-networks experiment biochemistry physiology simulation interesting stochastic-systems molecular-machinery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:46baae3d4e15/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gene-regulatory-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0688">
    <title>[1312.0688] Biophysical Fitness Landscapes for Transcription Factor Binding Sites</title>
    <dc:date>2014-03-30T11:24:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0688</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by the interactions between transcription factors (TFs) and their cognate genomic sites. Our study is informed by high-throughput in vitro measurements of TF-DNA binding interactions and by a comprehensive collection of genomic binding sites. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding energy for a collection of 12 yeast TFs, and show that the shape of the predicted fitness functions is in broad agreement with a simple thermodynamic model of two-state TF-DNA binding. However, the effective temperature of the model is not always equal to the physical temperature, indicating selection pressures in addition to biophysical constraints caused by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, showing that epistasis is common in evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience a spectrum of selection pressures depending on their position in the genome. These findings argue for the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.
]]></description>
<dc:subject>fitness-landscapes bioinformatics biochemistry biological-engineering experiment simulation interesting theoretical-biology of-course-it's-physics-now</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f78e8bd6839a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:of-course-it's-physics-now"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.4146">
    <title>[1312.4146] Unfolding kinetics of periodic DNA hairpins</title>
    <dc:date>2014-01-17T14:57:29+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.4146</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[DNA hairpin molecules with periodic base sequences can be expected to exhibit a regular coarse-grained free energy landscape (FEL) as function of the number of open base pairs and applied mechanical force. Using a commonly employed model, we first analyse for which types of sequences a particularly simple landscape structure is predicted, where forward and backward energy barriers between partly unfolded states are decreasing linearly with force. Stochastic unfolding trajectories for such molecules with simple FEL are subsequently generated by kinetic Monte Carlo simulations. Introducing probabilities that can be sampled from these trajectories, it is shown how the parameters characterising the FEL can be estimated. Already 300 trajectories, as typically generated in experiments, provide faithful results for the FEL parameters.
]]></description>
<dc:subject>structural-biology molecular-machinery experiment modeling DNA biochemistry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cd0bd3a00371/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:DNA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.7899">
    <title>[1310.7899] Evolution of autocatalytic sets in a competitive percolation model</title>
    <dc:date>2013-12-16T22:42:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.7899</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The evolution of autocatalytic sets (ACS) is a widespread process in biological, chemical and ecological systems which is of great significance in many applications, such as the evolution of new species or complex chemical organizations. In this paper, we propose a competitive model with a m-selection rule in which an abrupt emergence of a macroscopic independent ACS is observed. By numerical simulations, we find that the maximal increase of the size grows linearly with the system size. We analytically derive the threshold t{\alpha} where the abrupt jump happens and verify it by simulations. Moreover, our analysis explains how this giant independent ACS grows and reveals that, as the selection rule becomes more strict, the phase transition is dramatically postponed, and the number of the largest independent ACSs coexisting in the system increases accordingly. Our research work deepens the understanding of the evolution of ACS and should provide useful information for designing strategies to control the emergence of ACS in corresponding applications.
]]></description>
<dc:subject>origin-of-life chemical-reaction-networks artificial-life theoretical-biology self-organization nudge-targets simulation biochemistry consider:spatial-percolation-too</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:665f61e4cdda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:chemical-reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:spatial-percolation-too"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.8267">
    <title>[1310.8267] Realization of Morphing Logic Gates in a Repressilator with Quorum Sensing Feedback</title>
    <dc:date>2013-11-11T23:48:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.8267</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate how a genetic ring oscillator network with quorum sensing feedback can operate as a robust logic gate. Specifically we show how a range of logic functions, namely AND/NAND, OR/NOR and XOR/XNOR, can be realized by the system, thus yielding a versatile unit that can morph between different logic operations. We further demonstrate the capacity of this system to yield complementary logic operations in parallel. Our results then indicate the computing potential of this biological system, and may lead to bio-inspired computing devices.
]]></description>
<dc:subject>theoretical-biology systems-biology biochemistry regulation algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0eb2c858fe3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.3007">
    <title>[1305.3007] Probing water structures in nanopores using tunneling currents</title>
    <dc:date>2013-09-17T12:13:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.3007</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the effect of volumetric constraints on the structure and electronic transport properties of distilled water in a nanopore with embedded electrodes. Combining classical molecular dynamics simulations with quantum scattering theory, we show that the structural motifs water assumes inside the pore can be probed directly by tunneling. In particular, we show that the current does not follow a simple exponential curve at a critical pore diameter of about 8 {\AA}, rather it is larger than the one expected from simple tunneling through a barrier. This is due to a structural transition from bulk-like to "nanodroplet" water domains. Our results can be tested with present experimental capabilities to develop our understanding of water as a complex medium at nanometer length scales.
]]></description>
<dc:subject>molecular-machinery simulation biochemistry nanotechnology nudge-targets consider:design-substrate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d347927f9ed2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:design-substrate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.0069">
    <title>[1306.0069] Statistical Methods for Estimating Complexity from Competition Experiments between Two Populations</title>
    <dc:date>2013-06-07T11:30:45+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.0069</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Often a screening or selection experiment targets a cell or tissue, which presents many possible molecular targets and identifies a correspondingly large number of ligands. We describe a statistical method to extract an estimate of the complexity or richness of the set of molecular targets from competition experiments between distinguishable ligands, including aptamers derived from combinatorial experiments (SELEX or phage display). In simulations, the nonparametric statistic provides a robust estimate of complexity from a 100x100 matrix of competition experiments, which is clearly feasible in high-throughput format. The statistic and method are potentially applicable to other ligand binding situations.
]]></description>
<dc:subject>bioinformatics statistics complexity SELEX performance-measure biochemistry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:baf65a514f5a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:SELEX"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.6431">
    <title>[1207.6431] Optimal reconstruction of the folding landscape using differential energy surface analysis</title>
    <dc:date>2013-05-25T11:10:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.6431</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In experiments and in simulations, the free energy of a state of a system can be determined from the probability that the state is occupied. However, it is often necessary to impose a biasing potential on the system so that high energy states are sampled with sufficient frequency. The unbiased energy is typically obtained from the data using the weighted histogram analysis method (WHAM). Here we present differential energy surface analysis (DESA), in which the gradient of the energy surface, dE/dx, is extracted from data taken with a series of harmonic biasing potentials. It is shown that DESA produces a maximum likelihood estimate of the folding landscape gradient. DESA is demonstrated by analyzing data from a simulated system as well as data from a single-molecule unfolding experiment in which the end-to-end distance of a DNA hairpin is measured. It is shown that the energy surface obtained from DESA is indistinguishable from the energy surface obtained when WHAM is applied to the same data. Two criteria are defined which indicate whether the DESA results are self-consistent. It is found that these criteria can detect a situation where the energy is not a single-valued function of the measured reaction coordinate. The criteria were found to be satisfied for the experimental data analyzed, confirming that end-to-end distance is a good reaction coordinate for the experimental system. The combination of DESA and the optical trap assay in which a structure is disrupted under harmonic constraint facilitates an extremely accurate measurement of the folding energy surface.
]]></description>
<dc:subject>biochemistry protein-folding fitness-landscapes nudge-targets algorithms performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:33b681fa8b38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.7241">
    <title>[1304.7241] pH-dependent Response of Coiled Coils: A Coarse-Grained Molecular Simulation Study</title>
    <dc:date>2013-04-30T21:56:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.7241</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In a recent work we proposed a coarse-grained methodology for studying the response of peptides when simulated at different values of pH; in this work we extend the methodology to analyze the pH-dependent behavior of coiled coils. This protein structure presents a remarkable chain stiffness andis formed by two or more long helical peptides that are interacting like the strands of a rope. Chain length and rigidity are the key aspects needed to extend previous peptide interaction potentials to this particular case; however the original model is naturally recovered when the length or the ridigity of the simulated chain are reduced. We apply the model and discuss results for two cases: (a) the folding/unfolding transition of a generic coiled coil as a function of pH; (b) behavior of a specific sequence as a function of the acidity conditions. In this latter case results are compared with experimental data from the literature in order to comment about the consistency of our approach.
]]></description>
<dc:subject>biochemistry protein-folding structural-biology engineering-design biological-engineering nudge-targets simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8e7ba4a2acc9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.4312">
    <title>[1212.4312] pH-dependent coarse-grained model of peptides</title>
    <dc:date>2013-04-30T21:55:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.4312</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose the first, to our knowledge, coarse-grained modeling strategy for peptides where the effect of changes of the pH can be efficiently described. The idea is based on modeling the effects of the pH value on the main driving interactions. We use reference data from atomistic simulations and experimental databases and transfer its main physical features to the coarse-grained resolution according the principle of "consistency across the scales". The coarse-grained model is refined by finding a set of parameters that, when applied to peptides with different sequences and experimental properties, reproduces the experimental and atomistic data of reference. We use the such parameterized model for performing several numerical tests to check its transferability to other systems and to prove the universality of the related modeling strategy. We have tried systems with rather different response to pH variations, showing a highly satisfactory performance of the model.
]]></description>
<dc:subject>structural-biology protein-folding biochemistry models simulation interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8d8919003693/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1211.0662">
    <title>[1211.0662] Hidden Complexity in the Isomerization Dynamics of Holliday Junctions</title>
    <dc:date>2013-03-06T19:06:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.0662</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A plausible consequence of rugged energy landscapes of biomolecules is that functionally competent folded states may not be unique, as is generally assumed. Indeed, molecule-to-molecule variations in the dynamics of enzymes and ribozymes under folding conditions have recently been identified in single molecule experiments. However, systematic quantification and the structural origin of the observed complex behavior remain elusive. Even for a relatively simple case of isomerization dynamics in Holliday Junctions (HJs), molecular heterogeneities persist over a long observation time (Tobs ~ 40 sec). Here, using concepts in glass physics and complementary clustering analysis, we provide a quantitative method to analyze the smFRET data probing the isomerization in HJ dynamics. We show that ergodicity of HJ dynamics is effectively broken; as a result, the conformational space of HJs is partitioned into a folding network of kinetically disconnected clusters. While isomerization dynamics in each cluster occurs rapidly as if the associated conformational space is fully sampled, distinct patterns of time series belonging to different clusters do not interconvert on Tobs. Theory suggests that persistent heterogeneity of HJ dynamics is a consequence of internal multiloops with varying sizes and flexibilities frozen by Mg2+ ions. An annealing experiment using Mg2+ pulse that changes the Mg2+ cocentration from high to low to high values lends support to this idea by explicitly showing that interconversions can be driven among trajectories with different patterns.]]></description>
<dc:subject>molecular-design molecular-machinery biochemistry experiment protein-folding structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:95b009c64a0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.3453">
    <title>[1207.3453] Complex-linear invariants of biochemical networks</title>
    <dc:date>2012-08-29T11:11:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.3453</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The nonlinearities found in molecular networks usually prevent mathematical analysis of network behaviour, which has largely been studied by numerical simulation. This can lead to difficult problems of parameter determination. However, molecular networks give rise, through mass-action kinetics, to polynomial dynamical systems, whose steady states are zeros of a set of polynomial equations. These equations may be analysed by algebraic methods, in which parameters are treated as symbolic expressions whose numerical values do not have to be known in advance. For instance, an "invariant" of a network is a polynomial expression on selected state variables that vanishes in any steady state. Invariants have been found that encode key network properties and that discriminate between different network structures. Although invariants may be calculated by computational algebraic methods, such as Gr"obner bases, these become computationally infeasible for biologically realistic networks. Here, we exploit Chemical Reaction Network Theory (CRNT) to develop an efficient procedure for calculating invariants that are linear combinations of "complexes", or the monomials coming from mass action. We show how this procedure can be used in proving earlier results of Horn and Jackson and of Shinar and Feinberg for networks of deficiency at most one. We then apply our method to enzyme bifunctionality, including the bacterial EnvZ/OmpR osmolarity regulator and the mammalian 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase glycolytic regulator, whose networks have deficiencies up to four. We show that bifunctionality leads to different forms of concentration control that are robust to changes in initial conditions or total amounts. Finally, we outline a systematic procedure for using complex-linear invariants to analyse molecular networks of any deficiency.]]></description>
<dc:subject>reaction-networks systems-biology biochemistry simulation heuristics nudge-targets be-wise-linearize</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8bd61f1bab7a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:be-wise-linearize"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.1571">
    <title>[1206.1571] Steady-state fluctuations of a genetic feedback loop: an exact solution</title>
    <dc:date>2012-06-09T11:41:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.1571</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["…For the case where the degradation rate of bound and free protein is the same, our solution is at variance with a previous claim of an exact solution (Hornos et al, Phys. Rev. E {bf 72}, 051907 (2005) and subsequent studies). We show explicitly that this is due to an unphysical formulation of the underlying master equation in those studies."]]></description>
<dc:subject>unphysical biochemistry models analytical-models-of-messy-old-life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7bc6cbf9484d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:unphysical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analytical-models-of-messy-old-life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1105.2204">
    <title>[1105.2204] A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures</title>
    <dc:date>2012-06-08T15:21:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1105.2204</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Now this one is cool.]]></description>
<dc:subject>statistics inverse-problems biochemistry signal-processing algorithms machine-learning nudge-targets inference-of-things-that-aren't-toys</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:41e4d3ff67b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference-of-things-that-aren't-toys"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.1098">
    <title>[1206.1098] The interplay of intrinsic and extrinsic bounded noises in genetic networks</title>
    <dc:date>2012-06-08T15:09:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.1098</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. 

We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises."]]></description>
<dc:subject>biochemistry structural-biology reaction-networks biological-engineering noise its-complicated-inside-a-cell simulation nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5b5907dd51b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:noise"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:its-complicated-inside-a-cell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1010.4735">
    <title>[1010.4735] Exploring the Energy Landscapes of Protein Folding Simulations with Bayesian Computation</title>
    <dc:date>2012-01-05T13:38:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1010.4735</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Nested sampling is a Bayesian sampling technique developed to explore probability distributions lo- calised in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algo- rithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post-processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering (replica exchange). In this paper we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a Go-type force field of empirical potentials that were designed to stabilize secondary structure elements in room-temperature simulations. We demonstrate the method by conducting folding simulations on a number of small proteins which are commonly used for testing protein folding procedures: protein G, the SH3 domain of Src tyrosine kinase and chymotrypsin inhibitor 2. A topological analysis of the posterior samples is performed to produce energy landscape charts, which give a high level description of the potential energy surface for the protein folding simulations. These charts provide qualitative insights into both the folding process and the nature of the model and force field used.]]></description>
<dc:subject>structural-biology biochemistry modeling algorithms statistics metamodeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cc962a18eda9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metamodeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1109.2618">
    <title>[1109.2618] Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning</title>
    <dc:date>2012-01-05T13:34:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1109.2618</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
]]></description>
<dc:subject>machine-learning learning-from-data biochemistry computational-science nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4990c6d3af52/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.5794">
    <title>[1112.5794] BATMAN-an R package for the automated quantification of metabolites from NMR spectra using a Bayesian Model</title>
    <dc:date>2012-01-02T15:16:22+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.5794</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Motivation: NMR spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolite involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artifacts and limit immediate biological interpretation of models. Results: We present the Bayesian AuTomated Metabolite Analyser for NMR spectra (BATMAN), an R package which deconvolves peaks from 1-dimensional NMR spectra, automatically assigns them to specific metabolites and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov Chain Monte Carlo (MCMC) algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced mean estimation error compared with conventional numerical integration methods.]]></description>
<dc:subject>learning-from-data statistics modeling biochemistry nudge-targets image-segmentation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1a3062560e0a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1109.5389">
    <title>[1109.5389] Water drives peptide conformational transitions</title>
    <dc:date>2011-10-10T11:31:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1109.5389</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Transitions between metastable conformations of a dipeptide are investigated using classical molecular dynamics simulation with explicit water molecules. The distribution of the surrounding water at different moments before the transitions and the dynamical correlations of water with the peptide's configurational motions indicate that water is the main driving force of the conformational changes."]]></description>
<dc:subject>molecular-design systems-biology simulation intracellular-dynamics kinda-knew-this-a-long-time-ago biochemistry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:96f0382ba1af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:intracellular-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinda-knew-this-a-long-time-ago"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1105.4335">
    <title>[1105.4335] Physical approaches to the dynamics of genetic circuits: A tutorial</title>
    <dc:date>2011-10-04T14:05:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1105.4335</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Cellular behavior is governed by gene regulatory processes that are intrinsically dynamic and nonlinear, and are subject to non-negligible amounts of random fluctuations. Such conditions are ubiquitous in physical systems, where they have been studied for decades using the tools of statistical and nonlinear physics. The goal of this review is to show how approaches traditionally used in physics can help in reaching a systems-level understanding of living cells. To that end, we present an overview of the dynamical phenomena exhibited by genetic circuits and their functional significance. We also describe the theoretical and experimental approaches that are being used to unravel the relationship between circuit structure and function in dynamical cellular processes under the influence of noise, both at the single-cell level and in cellular populations, where intercellular coupling plays an important role."]]></description>
<dc:subject>systems-biology biological-engineering genetic-regulatory-networks emergent-design biochemistry overview</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:734a5a06dd70/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-regulatory-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:overview"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1007.2668">
    <title>[1007.2668] Protein abundances and interactions coevolve to promote functional complexes while suppressing non-specific binding</title>
    <dc:date>2010-07-30T11:57:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1007.2668</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous non-functional interactions between their proteins? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in crowded cellular environment. The model cell includes three independent pathways, whose topologies of PPI subnetworks are different, but whose functional concentrations equally contribute to cell's fitness. The model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: strengthening of functional interactions brings about hydrophobic surfaces, which make proteins prone to promiscuous binding.…"
]]></description>
<dc:subject>systems-biology biochemistry emergent-design systems-engineering molecular-machinery nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:343ad9444251/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.2791">
    <title>[1003.2791] Adaptive response and enlargement of dynamic range</title>
    <dc:date>2010-07-30T01:46:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.2791</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["…Here we study the quantitative relation between adaptive response and background compensation within a modeling framework. In contrast to the commonly held view, we show that any particular type of adaptive response is neither sufficient nor necessary for adaptive enlargement of dynamic range. In particular a precise adaptive response, where system activity is maintained at a constant level at steady state, does not ensure a large dynamic range neither in input signal nor in system output. A general mechanism for input dynamic range enlargement comes about from the activity-dependent modulation of protein responsiveness by multiple biochemical modification, regardless of the type of adaptive response it induces. Therefore hierarchical biochemical processes such as methylation and phosphorylation are natural candidates to induce this property in signalling systems."
]]></description>
<dc:subject>biochemistry molecular-machinery systems-biology dynamical-systems dynamic-control-prospects</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1f106a9453aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamic-control-prospects"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.4265">
    <title>[1006.4265] Modeling capsid self-assembly: Design and analysis</title>
    <dc:date>2010-06-29T00:12:59+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.4265</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A series of simulations aimed at elucidating the self-assembly dynamics of spherical virus capsids is described. This little-understood phenomenon is a fascinating example of the complex processes that occur in the simplest of organisms. The fact that different viruses adopt similar structural forms is an indication of a common underlying design, motivating the use of simplified, low-resolution models in exploring the assembly process. Several versions of a molecular dynamics approach are described. Polyhedral shells of different sizes are involved, the assembly pathways are either irreversible or reversible, and an explicit solvent is optionally included. …Among the key observations are that efficient growth proceeds by means of a cascade of highly reversible stages, and that while there are a large variety of possible partial assemblies, only a relatively small number of strongly bonded configurations are actually encountered."
]]></description>
<dc:subject>molecular-design virus biochemistry self-assembly simulation nudge-targets theoretical-biology biological-engineering</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:737c72315493/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:virus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1002.4273">
    <title>[1002.4273] Mutual information in time-varying biochemical systems</title>
    <dc:date>2010-06-28T14:00:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1002.4273</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[ME: what would 'well-designed' biochemical nets look like, if you evolved them in silico?

"The reliability with which a network can transmit a particular frequency component of the input signal tra- jectory is determined by the gain-to-noise ratio of the net- work as a function of frequency. For systems that obey the spectral addition rule [32], that is those for which downstream reactions do not affect the input signal, the gain-to-noise ratio is an intrinsic property of the processing network. For networks that do not obey the spectral addition rule the gain-to-noise ratio will be dependent on the statistics of the input signal. The mutual information between input and output signals, which quantifies the information which can be transmitted about a particular input ensemble, also depends on the particular choice of the input signal.…"
]]></description>
<dc:subject>biochemistry theoretical-biology molecular-design biological-engineering network-theory complexology nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:654e1ededccc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.1142">
    <title>[1005.1142] Reproduction of a Protocell by Replication of Minority Molecule in Catalytic Reaction Network</title>
    <dc:date>2010-05-12T12:17:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.1142</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["For understanding the origin of life, it is essential to explain the development of a compartmentalized structure, which undergoes growth and division, from a set of chemical reactions. In this study, a hypercycle with two chemicals that mutually catalyze each other is considered in order to show that the reproduction of a protocell with a growth-division process naturally occurs when the replication speed of one chemical is considerably slower than that of the other chemical. It is observed that the protocell divides after a minority molecule is replicated at a slow synthesis rate, and thus, a synchrony between the reproduction of a cell and molecule replication is achieved. The robustness of such protocells against the invasion of parasitic molecules is also demonstrated."
]]></description>
<dc:subject>origin-of-life artificial-life self-organization biochemistry autopoiesis abiogenesis simulation individuation</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:743d2e661e5b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autopoiesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abiogenesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:individuation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.1191">
    <title>[1005.1191] Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics</title>
    <dc:date>2010-05-12T12:00:04+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.1191</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["…We analyze the Lyapunov spectrum, determine the probability to find stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network and study how the frequency distributions of oscillatory modes of such system depend on the average connectivity."
]]></description>
<dc:subject>origin-of-life autocatalysis biochemistry theoretical-biology models nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:94deb5efcf5f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:origin-of-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:autocatalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://newscenter.lbl.gov/feature-stories/2010/05/10/untangling-quantum-entanglement/">
    <title>Untangling the Quantum Entanglement Behind Photosynthesis: Berkeley scientists shine new light on green plant secrets « Berkeley Lab News Center</title>
    <dc:date>2010-05-11T13:40:39+00:00</dc:date>
    <link>http://newscenter.lbl.gov/feature-stories/2010/05/10/untangling-quantum-entanglement/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The results of this study hold implications not only for the development of artificial photosynthesis systems as a renewable non-polluting source of electrical energy, but also for the future development of quantum-based technologies in areas such as computing – a quantum computer could perform certain operations thousands of times faster than any conventional computer."
]]></description>
<dc:subject>photosynthesis biochemistry biophysics molecular-design quantum-computing nanotechnology entanglement</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1b7bf18a64b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:photosynthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantum-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:entanglement"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0712.4248">
    <title>[0712.4248] Computer algebra in systems biology</title>
    <dc:date>2008-01-06T13:13:19+00:00</dc:date>
    <link>http://arxiv.org/abs/0712.4248</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The reinvention of the Block Diagram?
]]></description>
<dc:subject>bioinformatics systems-biology dynamics biochemistry</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:319aedac9d4d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>