ERA-IB 5 ECOYEAST_rjb Mastering the economics of adaptation through constraint-based modeling in yeast

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.

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.

Expected results and patents - we will deliver a methodology for large-scale genomics data
integration and modeling, that can be used and will be tested by our industrial partners. The
approach is generic and therefore of general use in industrial biotechnology. No patents are
expected from the modeling methodology per se, but as it is a powerful enabling technology for
biological discovery and innovation, there might be IP on optimization strategies. More directly,
findings for specific or general strain and/or process development of the succinic acid process, or
more general yeast fermentation might be achieved.

Preliminary exploitation plan - knowledge and expertise developed in this project, as well as
software and models, will be valuable assets for follow up in public-private partnerships. Software
will be developed under open-source agreement for academic use. None of the academic partners
have the ambition at this moment to set up a company to provide modeling services. All groups
involved have a large network with industrial partners to team up with to implement and apply the
developed technologies.