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<titleInfo><title>Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes</title></titleInfo>

  
  
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  <title>Rapid Communications</title>
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<name type="personal">
  <namePart type="given">Daniele</namePart>
  <namePart type="family">De Martino</namePart>
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  <namePart>International IST Postdoc Fellowship Programme</namePart>
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<abstract lang="eng">In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region.</abstract>

<originInfo><publisher>American Physical Society</publisher><dateIssued encoding="w3cdtf">2017</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Physical Review E</title></titleInfo>
  <identifier type="issn">2470-0045</identifier>
  <identifier type="arXiv">1707.00320</identifier>
  <identifier type="ISI">000418574400001</identifier><identifier type="doi">10.1103/PhysRevE.96.060401</identifier>
<part><detail type="volume"><number>96</number></detail><detail type="issue"><number>6</number></detail>
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<short>D. De Martino, Physical Review E 96 (2017).</short>
<chicago>De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” &lt;i&gt;Physical Review E&lt;/i&gt;. American Physical Society, 2017. &lt;a href=&quot;https://doi.org/10.1103/PhysRevE.96.060401&quot;&gt;https://doi.org/10.1103/PhysRevE.96.060401&lt;/a&gt;.</chicago>
<ama>De Martino D. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. &lt;i&gt;Physical Review E&lt;/i&gt;. 2017;96(6). doi:&lt;a href=&quot;https://doi.org/10.1103/PhysRevE.96.060401&quot;&gt;10.1103/PhysRevE.96.060401&lt;/a&gt;</ama>
<ieee>D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” &lt;i&gt;Physical Review E&lt;/i&gt;, vol. 96, no. 6. American Physical Society, 2017.</ieee>
<mla>De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” &lt;i&gt;Physical Review E&lt;/i&gt;, vol. 96, no. 6, 060401, American Physical Society, 2017, doi:&lt;a href=&quot;https://doi.org/10.1103/PhysRevE.96.060401&quot;&gt;10.1103/PhysRevE.96.060401&lt;/a&gt;.</mla>
<ista>De Martino D. 2017. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 96(6), 060401.</ista>
<apa>De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. &lt;i&gt;Physical Review E&lt;/i&gt;. American Physical Society. &lt;a href=&quot;https://doi.org/10.1103/PhysRevE.96.060401&quot;&gt;https://doi.org/10.1103/PhysRevE.96.060401&lt;/a&gt;</apa>
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