Fundamental Escherichia coli biochemical pathways for biomass and energy production:: Creation of overall flux states
Fundamental Escherichia coli biochemical pathways for biomass and energy production:: Creation of overall flux states
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DOI：
10.1002/bit.20044
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发表时间：
2004-04-20
影响因子：
3.8
通讯作者：
Srienc, F
中科院分区：
文献类型：
--
作者：
Carlson, R;Srienc, F
我们之前已经表明，最有效的细胞生长的新陈代谢可以通过两种基本模式的组合来实现。一种模式产生生物质，而第二种模式仅产生能量。四种最有效的生物质和能量途径对的特性会根据氧气限制的程度而变化。针对不同生长条件的此类途径对的识别为维持能量产生提供了基于途径的解释。对于给定的生长速率，实验性有氧葡萄糖消耗速率可用于估计每种途径类型对总体代谢通量模式的贡献。然后，所有代谢通量完全由定义所有营养物消耗和代谢物分泌率的相关途径的化学计量决定。我们在这里提出的方程允许在不同氧胁迫水平下最佳、葡萄糖限制大肠杆菌生长的独特路径的基础上计算网络通量。预测的葡萄糖和氧摄取率以及一些代谢物分泌率与支持所提出方法的有效性的实验观察结果非常一致。整个最有效的稳态代谢率结构由所开发的方程明确定义，无需额外的计算机模拟。该方法通常可用于通过预测简明的、基于途径的代谢率结构来分析和解释基因组数据。 (C) 2004 年 Wiley 期刊公司。
We have previously shown that the metabolism for most efficient cell growth can be realized by a combination of two types of elementary modes. One mode produces biomass while the second mode generates only energy. The identity of the four most efficient biomass and energy pathway pairs changes, depending on the degree of oxygen limitation. The identification of such pathway pairs for different growth conditions offers a pathway-based explanation of maintenance energy generation. For a given growth rate, experimental aerobic glucose consumption rates can be used to estimate the contribution of each pathway type to the overall metabolic flux pattern. All metabolic fluxes are then completely determined by the stoichiometries of involved pathways defining all nutrient consumption and metabolite secretion rates. We present here equations that permit computation of network fluxes on the basis of unique pathways for the case of optimal, glucose-limited Escherichia coli growth under varying levels of oxygen stress. Predicted glucose and oxygen uptake rates and some metabolite secretion rates are in remarkable agreement with experimental observations supporting the validity of the presented approach. The entire most efficient, steady-state, metabolic rate structure is explicitly defined by the developed equations without need for additional computer simulations. The approach should be generally useful for analyzing and interpreting genomic data by predicting concise, pathway-based metabolic rate structures. (C) 2004 Wiley Periodicals, Inc.