指数增长微生物种群适应度与异质性的关系
Relationship between fitness and heterogeneity in exponentially growing microbial populations
摘要：
尽管存在主要的环境和遗传差异，但已知微生物代谢网络在非常不同的生物体中产生一致的生理结果。这种显著的稳健性表明，至少在细菌中，代谢活动可能受到普遍原则的指导。生长率等进化目标函数的约束优化已经成为细菌代谢研究的关键理论假设。虽然在许多情况下在概念上和实际上都很有用，但某些功能被优化的想法很难在数据中得到验证。此外，在微生物种群的实验中观察到的单细胞变异性的高度，如何使最优性协调一致，这一点并不总是很清楚。为了阐明这些问题，我们开发了一个逆向建模框架，该框架通过从数据中对代谢表型分布进行最大熵推断，将细胞群体（由平均单细胞生长速率表示）的适应性与潜在的代谢变异性联系起来。虽然没有明确的目标函数出现，我们发现，随着介质变得更加丰富，大肠杆菌种群的适应性和推断的变异性遵循，并慢慢接近理论上的最佳界限定义的变异性在给定的健身最小减少。这些结果表明，细菌的代谢可能是至关重要的人口水平的增长和异质性之间的权衡塑造。
英文摘要：
Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the Maximum-Entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity.
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