Erickson David W, Schink Severin J, Patsalo Vadim, Williamson James R, Gerland Ulrich, Hwa Terence
Department of Physics, University of California San Diego, La Jolla, California 92093, USA.
Physics of Complex Biosystems, Physics Department, Technical University of Munich, 85748 Garching, Germany.
Nature. 2017 Nov 2;551(7678):119-123. doi: 10.1038/nature24299. Epub 2017 Oct 25.
A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms and they remain a topic of active investigation. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.
系统生物学面临的一个重大挑战是,从潜在的分子相互作用出发,预测生命系统对扰动的动力学响应。长期以来,营养环境的变化一直被用于研究微生物中的调控和适应现象,并且它们仍然是活跃的研究课题。尽管对于响应施加的扰动而调控关键代谢过程的分子相互作用已经了解很多,但对于自下而上的预测建模而言,这些相互作用的量化还不够充分。在此,我们开发了一种自上而下的方法,将最近建立的粗粒度蛋白质组分配模型从稳态生长扩展到动力学领域。仅利用潜在调控过程的定性知识并施加通量平衡条件,我们推导出了一个细菌生长转变的定量模型,该模型独立于难以获取的动力学参数。由此产生的通量控制调控模型能够准确预测基因表达和生物量积累响应碳源上调和下调(例如,二次生长转变)的时间进程,且无需可调参数。正如该模型所预测并经定量蛋白质组学验证的那样,由于蛋白质合成分配的刚性策略,细胞在响应营养物质变化时表现出次优的恢复动力学,该策略并非旨在缓解特定的代谢瓶颈。我们的方法不依赖于动力学参数,因此指向了一个理论框架,用于在不详细了解潜在生化反应的情况下描述广泛的此类动力学过程。