Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria.
Phys Rev E. 2017 Dec;96(6-1):060401. doi: 10.1103/PhysRevE.96.060401. Epub 2017 Dec 21.
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.
在这项工作中，考虑了在约束增长率的第一和第二矩的情况下，代谢网络稳态空间中的最大熵分布。通过考虑对平均生长（优化）及其波动（异质性）的控制，出现了快速和慢速表型共存，具有双峰通量分布。这应用于大肠杆菌的碳分解代谢核心，它量化了缓慢生长表型的代谢活性，并提供了具有代谢通量的定量图谱，从而有可能从通量数据中检测到共存。对标准条件下大肠杆菌培养物数据的初步分析表明，推断参数的简并性扩展到共存区域。
