Laboratory for Molecular Systems Biology, Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa.
Centre for Complex Systems in Transition, Stellenbosch University, Stellenbosch, South Africa.
PLoS One. 2018 Nov 28;13(11):e0207983. doi: 10.1371/journal.pone.0207983. eCollection 2018.
High-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system's components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions. Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic flux or steady-state concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control. These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.
代谢系统的高级行为是由众多分子组成部分的特性和相互作用产生的。因此，基于系统的组成部分来完全理解代谢行为是一项艰巨的任务。这个问题可以通过构建和随后分析代谢途径的动力学模型来解决，因为这些模型旨在捕捉系统组成部分及其相互作用的所有相关特性。符号控制分析是一种用于分析途径模型的框架，以便达到对其行为的机械理解。通过提供系统属性（如代谢通量或稳态浓度）对单个反应属性的敏感性的代数表达式，它允许将高级行为追溯到这些低级组件。在这里，我们将该方法应用于乳球菌乳亚种中丙酮酸分支代谢的模型，以解释先前观察到的对底物浓度增加的负通量响应。通过这种方法，我们首先能够表明通量对反应速率变化的敏感性（由通量控制系数表示）由途径的各个代谢分支决定，其次，单个反应速率对其底物的敏感性（由弹性系数表示）如何对这种通量控制做出贡献。我们还分别量化了酶结合和质量作用对酶弹性的贡献，这允许更精细地理解通量控制。这些分析工具使我们能够分析代谢模型的控制特性，并对每个酶对这种控制的定量贡献有一个机械的理解。我们的分析提供了符号控制分析一般原理的描述能力的一个示例。
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