Quantifying the entropic cost of cellular growth control

Abstract

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale models. Results obtained for E. coli and human cells are consistent with experiments and point to different regulatory strategies by which growth can be fostered or repressed. Moreover we explicitly connect the `inverse temperature' that controls MaxEnt distributions to the growth dynamics, showing that the initial size of a colony may be crucial in determining how an exponentially growing population organizes the phenotypic space.

3 pages

References in corpus (2)

• Constrained Allocation Flux Balance Analysis
• Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle

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