Statistical mechanics for metabolic networks during steady state growth

D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications 9 (2018).

Download
OA 2018_NatureComm_DeMartino.pdf 1.04 MB

Journal Article | Published | English

Scopus indexed
Abstract
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.
Publishing Year
Date Published
2018-07-30
Journal Title
Nature Communications
Volume
9
Issue
1
Article Number
2988
IST-REx-ID
161

Cite this

De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-05417-9
De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., & Tkačik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05417-9
De Martino, Daniele, Andersson Anna Mc, Tobias Bergmiller, Calin C Guet, and Gašper Tkačik. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” Nature Communications 9, no. 1 (2018). https://doi.org/10.1038/s41467-018-05417-9.
D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1, 2018.
De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. 2018. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 9(1).
De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” Nature Communications, vol. 9, no. 1, 2988, Springer Nature, 2018, doi:10.1038/s41467-018-05417-9.
All files available under the following license(s):
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0):
Main File(s)
Access Level
OA Open Access
Date Uploaded
2018-12-17
MD5 Checksum
3ba7ab27b27723c7dcf633e8fc1f8f18


Export

Marked Publications

Open Data IST Research Explorer

Search this title in

Google Scholar