Modeling of Biological Processes, BioQuant/Center for Organismal Studies Heidelberg, Heidelberg University, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU Amsterdam, De Boelelaan 1085, NL-1081HZ Amsterdam, The Netherlands.
Institute of Medical Microbiology, Virology, and Hygiene, Rostock University Medical Center, Schillingallee 70, D-18055 Rostock, Germany.
J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10.
In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.
深入了解微生物的生长对于生物技术的新进展的发展以及对抗微生物病原体至关重要。特定条件下的蛋白质组表达是微生物生理学和生长的核心。许多过程都依赖于蛋白质的表达，因此，使用基于基因组规模的代谢模型对微生物代谢进行全细胞分析是一种有吸引力的工具集，可以研究微生物及其群落的行为。然而，包含大分子表达的基因组规模模型仍然是复杂的：这些模型的概念和计算复杂性严重限制了它们的潜在应用。在需要替代品的情况下，我们在这里重新审视了一些以前尝试创建代谢和大分子表达的基因组规模模型的尝试，以开发一种将蛋白质丰度和周转率成本整合到传统基因组规模模型中的新框架。我们表明，这种大肠杆菌的模型能够成功地以生长条件依赖的方式再现实验确定的代谢适应。此外，该模型可用于研究不同生长环境中蛋白质机器的未充分利用情况。值得注意的是，我们通过明确考虑蛋白质翻译的成本，获得了对通量分布的强烈改进预测。这一发现反过来表明蛋白质翻译是细胞生长的主要调节中心。
