Lafontaine Rivera Jimmy G, Lee Yun, Liao James C
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, California 90095, USA.
Integr Biol (Camb). 2015 Aug;7(8):895-903. doi: 10.1039/c4ib00257a.
Natural and synthetic metabolic pathways need to retain stability when faced against random changes in gene expression levels and kinetic parameters. In the presence of large parameter changes, a robust system should specifically avoid moving to an unstable region, an event that would dramatically change system behavior. Here we present an entropy-like index, denoted as S, for quantifying the bifurcational robustness of metabolic systems against loss of stability. We show that S enables the optimization of a metabolic model with respect to both bifurcational robustness and experimental data. We then demonstrate how the coupling of ensemble modeling and S enables us to discriminate alternative designs of a synthetic pathway according to bifurcational robustness. Finally, we show that S enables the identification of a key enzyme contributing to the bifurcational robustness of yeast glycolysis. The different applications of S demonstrated illustrate the versatile role it can play in constructing better metabolic models and designing functional non-native pathways.
当面对基因表达水平和动力学参数的随机变化时，天然和合成代谢途径需要保持稳定性。在参数大幅变化的情况下，一个稳健的系统应特别避免进入不稳定区域，因为这一事件会显著改变系统行为。在此，我们提出一种类似熵的指标，记为S，用于量化代谢系统对稳定性丧失的分支鲁棒性。我们表明，S能够使代谢模型在分支鲁棒性和实验数据方面都得到优化。然后，我们展示了集成建模与S的结合如何使我们能够根据分支鲁棒性来区分合成途径的替代设计。最后，我们表明，S能够识别出对酵母糖酵解的分支鲁棒性有贡献的关键酶。所展示的S的不同应用说明了它在构建更好的代谢模型和设计功能性非天然途径中可以发挥的多种作用。
