arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1601.03243q-bio.MNcond-mat.dis-nnphysics.bio-phq-bio.PE
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细菌代谢中的生长与熵:大肠杆菌经验生长率分布背后的表型权衡。杆菌

Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli

Daniele De Martino, Fabrizio Capuani, Andrea De Martino

摘要:

细胞代谢的基因组规模模型的解决方案空间提供了物理上可行的通量配置和细胞代谢表型之间的映射,在最基本的水平上,由相应的生长速率。通过对E.大肠杆菌的代谢网络,我们表明,最近在实验中获得的经验增长率分布在单细胞分辨率可以解释在快速增长的表型和缓慢增长的较高的熵的较高的健身之间的权衡。在此基础上,我们提出了一个最小的模型,一个大的细菌种群的进化,捕捉这种权衡。在这样的框架中,在实验中观察到的缩放关系编码为与最大可实现生长速率的相同距离、相同的生长速率最大化程度和/或相同的表型变化速率。基于基因组尺度的代谢网络重建,这些结果允许多个含义和扩展,尽管基本的概念简单。

英文摘要:

The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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