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

ReferenceBB/M025748/1
Principal Investigator / Supervisor Professor Simon Hubbard
Co-Investigators /
Co-Supervisors
Institution The University of Manchester
DepartmentSchool of Biological Sciences
Funding typeResearch
Value (£) 271,256
StatusCompleted
TypeResearch Grant
Start date 01/04/2015
End date 31/07/2018
Duration40 months

Abstract

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.

Summary

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.

Impact Summary

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.
Committee Research Committee C (Genes, development and STEM approaches to biology)
Research TopicsIndustrial Biotechnology, Microbiology, Systems Biology
Research PriorityX – Research Priority information not available
Research Initiative ERA Industrial Biotechnology (ERA-IB) [2013-2014]
Funding SchemeX – not Funded via a specific Funding Scheme
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