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10.1038/s41467-018-05417-9 http://scihub22266oqcxt.onion/10.1038/s41467-018-05417-9 C6065372!6065372!30061556     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2018 ; 9 (ä): ä Nephropedia Template TP {{tp|p=30061556|t=2018. Statistical mechanics for metabolic networks during steady state growth |pdf=|usr=}}{{30061556}} gab.com Text Statistical mechanics for metabolic networks during steady state growth JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=30061556 #FOAvip Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells. Twit Text FOAVip Statistical mechanics for metabolic networks during steady state growth ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=30061556 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30061556 #FOAvip English Wikipedia <ref>{{Cite pmid|30061556}}</ref> Statistical mechanics for metabolic networks during steady state growth #MMPMID30061556 De Martino D; MC Andersson A; Bergmiller T; Guet CC; Tka?ik G Nat Commun 2018[]; 9 (ä): ä PMID30061556show ga Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells. äStatistical mechanics for metabolic networks during steady state growt(epmc).pdf DeepDyve Pubget Overpricing