Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
Bioinformatics. 2018 Apr 15;34(8):1363-1371. doi: 10.1093/bioinformatics/btx769.
Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks.
Using kinetic models of metabolism, we assess the change in the bistable characteristics of the network, and suggest designs based on perturbations to the positive feedback loop to enable the network to produce at its theoretical maximum rate. We show that the most optimal production design in parameter space, for a small bistable metabolic network, may exist at the boundary of the bistable region separating it from the monostable region of low product fluxes. The results of our analysis can be broadly applied to other bistable metabolic networks with similar positive feedback network topologies. This can complement existing model-based design strategies by providing a smaller number of feasible designs that need to be tested in vivo.
http://lmse.biozone.utoronto.ca/downloads/.
krishna.mahadevan@utoronto.ca.
Supplementary data are available at Bioinformatics online.
由于存在正反馈回路等调节基序，代谢可以表现出双稳态等动态现象。作为细胞工厂，具有双稳态代谢的微生物在两个稳定的稳态分别具有高和低的产物通量。代谢调控和网络动态的排除限制了拟稳态化学计量模型检测双稳态存在的能力，并且无法可靠地评估代谢网络设计扰动的结果。
我们使用代谢的动力学模型来评估网络双稳态特性的变化，并基于对正反馈回路的扰动来提出设计方案，以实现网络的理论最大速率。我们表明，对于一个小的双稳态代谢网络，在将其与低产物通量的单稳态区域分开的双稳态区域边界处，可能存在参数空间中最优的生产设计。我们分析的结果可以广泛应用于具有类似正反馈网络拓扑的其他双稳态代谢网络。这可以通过提供数量更少的需要在体内测试的可行设计来补充现有的基于模型的设计策略。
http://lmse.biozone.utoronto.ca/downloads/。
krishna.mahadevan@utoronto.ca。
补充数据可在Bioinformatics 在线获得。
