ERA-IB 5. ECOYEAST SJH: Mastering the economics of adaptation through constraint-based modeling in yeast (Hubbard)

活细胞进化出了适应环境条件或承受突变的非凡能力。在生物技术中，这损害了代谢工程的成功，并导致工程菌株的不稳定性。“功能基因组学”使得对细胞的许多成分进行经济有效的测量成为可能。然而，我们仍然无法理解它们的相互作用是如何导致细胞功能和适应的。然而，很明显，物理和（生物）化学对适应和进化施加了很强的限制。这些限制限制了细胞可以合成的蛋白质总量，并影响了细胞如何在其过程中分配有限的资源以优化适应性（“细胞经济学”）。这些知识对于更好的代谢工程策略非常重要，这些策略考虑到新基因和途径对细胞经济学的影响，从而开发出具有高产量的工艺，从而实现具有成本效益的生物基化学品和生物燃料。在本提案中，我们将开发一个建模框架，允许将大型数据集集成到综合机制模型中。这些模型是基因组规模的，将能够计算实施代谢工程策略的成本和收益。经济模型将用于以两种方式提供概念验证：(i)作为数据整合和解释适应性反应的工具；（ii）作为预测工具，通过优化来预测更现实的理论产量，并通过探索代谢工程场景。这将由我们的工业合作伙伴帝斯曼和罗盖特提供的用户案例进行测试，涉及琥珀酸盐生产，这是一种多功能C4二酸，具有许多潜在的应用，例如在聚合物和树脂中。

Living cells evolved a remarkable ability to adapt to environmental conditions, or to withstand mutations. In biotechnology, this compromises success in metabolic engineering and causes instability of engineered strains. "Functional genomics" has allowed the cost-effective measurement of many of the components of the cell. However, we still mostly fail to understand how their interactions lead to cellular function and adaptation. It becomes clear, however, that physics and (bio)chemistry impose strong constraints on adaptation and evolution. Such constraints limit the total amount of protein that a cell can synthesize, and impact on how it should partition that limited resource over its processes to optimize fitness ("cellular economics"). Such knowledge is important to come with better metabolic engineering strategies that take into account the impact of novel genes and pathways on cellular economics, to develop processes with high yields that enable cost-effective bio-based chemicals and biofuels. In this proposal we will develop a modeling framework that will allow the integration of large data sets into comprehensive mechanistic models. These models are of genome-scale and will be able to compute the costs and benefits of implementing metabolic engineering strategies. The economic models will be used to provide proof-of-concept in two ways: (i) as tools for data integration and interpretation of adaptive responses; (ii) as predictive tool, through optimisation to predict more realistic theoretical yields and through exploration of metabolic engineering scenarios. This will be tested by a user case provided by our industrial partners, DSM and Roquette, involving succinate production, a versatile C4 diacid with a lot of potential applications, e.g. in polymers and resins.