Identifying essential genes in Escherichia coli from a metabolic optimization principle
Identifying essential genes in Escherichia coli from a metabolic optimization principle
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DOI：
10.1073/pnas.0813229106
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发表时间：
2009-02-24
影响因子：
11.1
通讯作者：
Castillo, Isaac Perez
中科院分区：
文献类型：
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作者：
Martelli, Carlotta;De Martino, Andrea;Castillo, Isaac Perez
从化学计量学和基本生化网络的拓扑结构来理解细胞代谢中反应通量的组织是系统生物学的一个中心问题。在这项任务中，重要的是要设计合理的近似方案，只依赖于化学计量数据，因为全尺度动力学方法是计算负担得起的，只有小网络（如。例如，在一个实施例中，红细胞，大约50个反应）。常用的方法是基于找到满足代谢物质量平衡条件的固定通量配置，通常将它们与局部优化规则（例如，例如，在一个实施例中，生物质产量的最大化）以将解空间的大小减小到单个点。这些方法已被广泛应用，并已证明能够在特定条件下重现相对简单的生物体的实验结果。在这里，我们定义和研究一个基于约束的细胞代谢模型，其中既不假设质量平衡，也不假设通量平稳性，相关的通量配置优化系统的全局增长。在大肠杆菌的情况下，稳定的通量状态恢复的解决方案，虽然违反了质量平衡条件的一些代谢产物，这意味着后者的非零净生产。这样的解决方案进一步证明提供了正确的统计流量的细菌E。大肠杆菌在不同的环境中，并比较以及与现有的实验证据的个人通量。保守代谢池在确定生长速率和通量变异性方面起着关键作用。最后，我们能够将现象学基因本质性与“冻结”通量（即，允许变异性较小的通量）在E.大肠杆菌代谢
Understanding the organization of reaction fluxes in cellular metabolism from the stoichiometry and the topology of the underlying biochemical network is a central issue in systems biology. In this task, it is important to devise reasonable approximation schemes that rely on the stoichiometric data only, because full-scale kinetic approaches are computationally affordable only for small networks (e. g., red blood cells, approximate to 50 reactions). Methods commonly used are based on finding the stationary flux configurations that satisfy mass-balance conditions for metabolites, often coupling them to local optimization rules (e. g., maximization of biomass production) to reduce the size of the solution space to a single point. Such methods have been widely applied and have proven able to reproduce experimental findings for relatively simple organisms in specific conditions. Here, we define and study a constraint-based model of cellular metabolism where neither mass balance nor flux stationarity are postulated and where the relevant flux configurations optimize the global growth of the system. In the case of Escherichia coli, steady flux states are recovered as solutions, although mass-balance conditions are violated for some metabolites, implying a nonzero net production of the latter. Such solutions furthermore turn out to provide the correct statistics of fluxes for the bacterium E. coli in different environments and compare well with the available experimental evidence on individual fluxes. Conserved metabolic pools play a key role in determining growth rate and flux variability. Finally, we are able to connect phenomenological gene essentiality with "frozen'' fluxes (i.e., fluxes with smaller allowed variability) in E. coli metabolism.