De Martino Daniele, Capuani Fabrizio, De Martino Andrea
Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria.
Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy.
Phys Rev E. 2017 Jul;96(1-1):010401. doi: 10.1103/PhysRevE.96.010401. Epub 2017 Jul 10.
Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximum-entropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genome-scale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximum-entropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence.
将活细胞组织其新陈代谢的方式视为物理系统的相空间，调控可被视为通过选择在某种意义上最优的特定细胞构型来降低该空间熵的能力。在这里，我们通过对潜在代谢表型空间采用最大熵方法来量化控制细胞生长速率所需的调控量，其中一种构型对应于基因组规模模型所描述的代谢通量模式。我们通过一个相图将一群细胞实现的平均生长速率与实现该生长速率所需的最小代谢调控量联系起来，该相图突出了生长抑制在（调控方面）可能与生长促进一样代价高昂。此外，我们基于潜在的生长动力学对控制最大熵分布的逆温度β给出一种解释。具体而言，我们表明细胞群体的β渐近值预计取决于：（i）环境的承载能力，（ii）菌落的初始大小，以及（iii）接种物所采样的概率分布。发现大肠杆菌和人类细胞所获得的结果与经验证据显著一致。
