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    <title>Pinboard (mraginsky)</title>
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    <description>recent bookmarks from mraginsky</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://www.biorxiv.org/content/10.1101/2020.09.19.304584v1"/>
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  </channel><item rdf:about="https://www.biorxiv.org/content/10.1101/2020.09.19.304584v1">
    <title>On the Mathematics of RNA Velocity I: Theoretical Analysis | bioRxiv</title>
    <dc:date>2024-12-09T19:23:32+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2020.09.19.304584v1</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The RNA velocity provides a new avenue to study the stemness and lineage of cells in the development in scRNA-seq data analysis. Some promising extensions of it are proposed and the community is experiencing a fast developing period. However, in this stage, it is of prime importance to revisit the whole process of RNA velocity analysis from the mathematical point of view, which will help to understand the rationale and drawbacks of different proposals. The current paper is devoted to this purpose. We present a thorough mathematical study on the RNA velocity model from dynamics to downstream data analysis. We derived the analytical solution of the RNA velocity model from both deterministic and stochastic point of view. We presented the parameter inference framework based on the maximum likelihood estimate. We also derived the continuum limit of different downstream analysis methods, which provides insights on the construction of transition probability matrix, root and endingcells identification, and the development routes finding. The overall analysis aims at providing a mathematical basis for more advanced design and development of RNA velocity type methods in the future.]]></description>
<dc:subject>papers to-read biology systems-biology cells genomics dynamical-systems</dc:subject>
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<dc:identifier>https://pinboard.in/u:mraginsky/b:5e24c4613977/</dc:identifier>
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    <title>[1012.4863] Dynamical quorum-sensing and synchronization of nonlinear oscillators coupled through an external medium</title>
    <dc:date>2011-01-03T23:30:00+00:00</dc:date>
    <link>http://arxiv.org/abs/1012.4863</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["Many biological and physical systems exhibit population-density dependent transitions to synchronized oscillations in a process often termed "dynamical quorum sensing". Synchronization frequently arises through chemical communication via signaling molecules distributed through an external media. We study a simple theoretical model for dynamical quorum sensing: a heterogenous population of limit-cycle oscillators diffusively coupled through a common media. We show that this model exhibits a rich phase diagram with four qualitatively distinct mechanisms fueling population-dependent transitions to global oscillations, including a new type of transition we term "dynamic death". We derive a single pair of analytic equations that allows us to calculate all phase boundaries as a function of population density and show that the model reproduces many of the qualitative features of recent experiments of BZ catalytic particles as well as synthetically engineered bacteria."
]]></description>
<dc:subject>papers to-read biology dynamical-systems cells control-theory feedback</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9f7d470791f6/</dc:identifier>
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    <title>Strategies for cellular decision-making. [Mol Syst Biol. 2009] - PubMed result</title>
    <dc:date>2010-11-25T03:38:47+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/pubmed/19920811</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read systems-biology decision-making cells bayesian-learning</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:397fb1aa6b92/</dc:identifier>
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