Schwartz Jean-Marc, Kanehisa Minoru
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
BMC Bioinformatics. 2006 Apr 3;7:186. doi: 10.1186/1471-2105-7-186.
Elementary mode analysis of metabolic pathways has proven to be a valuable tool for assessing the properties and functions of biochemical systems. However, little comprehension of how individual elementary modes are used in real cellular states has been achieved so far. A quantitative measure of fluxes carried by individual elementary modes is of great help to identify dominant metabolic processes, and to understand how these processes are redistributed in biological cells in response to changes in environmental conditions, enzyme kinetics, or chemical concentrations.
Selecting a valid decomposition of a flux distribution onto a set of elementary modes is not straightforward, since there is usually an infinite number of possible such decompositions. We first show that two recently introduced decompositions are very closely related and assign the same fluxes to reversible elementary modes. Then, we show how such decompositions can be used in combination with kinetic modelling to assess the effects of changes in enzyme kinetics on the usage of individual metabolic routes, and to analyse the range of attainable states in a metabolic system. This approach is illustrated by the example of yeast glycolysis. Our results indicate that only a small subset of the space of stoichiometrically feasible steady states is actually reached by the glycolysis system, even when large variation intervals are allowed for all kinetic parameters of the model. Among eight possible elementary modes, the standard glycolytic route remains dominant in all cases, and only one other elementary mode is able to gain significant flux values in steady state.
These results indicate that a combination of structural and kinetic modelling significantly constrains the range of possible behaviours of a metabolic system. All elementary modes are not equal contributors to physiological cellular states, and this approach may open a direction toward a broader identification of physiologically relevant elementary modes among the very large number of stoichiometrically possible modes.
代谢途径的基本模式分析已被证明是评估生化系统特性和功能的宝贵工具。然而,到目前为止,对于单个基本模式在实际细胞状态中的使用方式了解甚少。对单个基本模式所承载通量的定量测量,有助于识别主要的代谢过程,并理解这些过程如何在生物细胞中随着环境条件、酶动力学或化学浓度的变化而重新分布。
将通量分布有效地分解到一组基本模式上并非易事,因为通常存在无数种可能的分解方式。我们首先表明,最近引入的两种分解方式密切相关,并且为可逆基本模式分配相同的通量。然后,我们展示了如何将这些分解方式与动力学建模相结合,以评估酶动力学变化对各个代谢途径使用的影响,并分析代谢系统中可达到状态的范围。以酵母糖酵解为例说明了这种方法。我们的结果表明,即使模型的所有动力学参数允许有较大的变化区间,糖酵解系统实际上也只达到了化学计量学上可行的稳态空间的一小部分。在八种可能的基本模式中,标准糖酵解途径在所有情况下都占主导地位,并且在稳态下只有另一种基本模式能够获得显著的通量值。
这些结果表明,结构建模和动力学建模相结合显著限制了代谢系统可能行为的范围。并非所有基本模式对生理细胞状态的贡献都相同,这种方法可能为在大量化学计量学上可能的模式中更广泛地识别生理相关基本模式开辟一条道路。