Growth against entropy in bacterial metabolism: The phenotypic trade-off behind empirical growth rate distributions in E. coli. Martino, D., Capuani, F., & Martino, A. Physical Biology, Institute of Physics Publishing, 2016. cited By 22![Growth against entropy in bacterial metabolism: The phenotypic trade-off behind empirical growth rate distributions in E. coli [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity. © 2016 IOP Publishing Ltd.
@ARTICLE{Martino2016,
author={Martino, D.D. and Capuani, F. and Martino, A.D.},
title={Growth against entropy in bacterial metabolism: The phenotypic trade-off behind empirical growth rate distributions in E. coli},
journal={Physical Biology},
year={2016},
volume={13},
number={3},
doi={10.1088/1478-3975/13/3/036005},
art_number={036005},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979649263&doi=10.1088%2f1478-3975%2f13%2f3%2f036005&partnerID=40&md5=0e840d3f4b565ff2be0e657c3494a4f7},
abstract={The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity. © 2016 IOP Publishing Ltd.},
publisher={Institute of Physics Publishing},
issn={14783967},
pubmed_id={27232645},
document_type={Article},
source={Scopus},
}
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