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Project Topic
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The bioprocess industry needs new efficient and sustainable routes to manufacture bio-products. Bioprocesses use the power and versatility of nature via microorganisms that make bio-products from renewable feedstocks. Micro-organisms can very well be engineered as efficient cell factories. However, the gap between the cell environment at lab and production scales is causing gross resource and asset utilization inefficiencies, and is a barrier to fast and successful scale-up. This bottleneck has become one of the most prominent risks for the bio-economy, moving from innovation (supported by the revolutions in metabolic engineering and molecular biology) to commercialization. Today, scale-up in industry is still driven by physical guidelines or heuristic approaches but does not consider individual cellular properties for ab initio and in silico design. Previous pioneering studies could not exploit the full predictive potential because details of metabolic and transcriptional regulatory responses were not yet known and computational capacities limiting. In recent years, advanced tools have been developed which, taken together, offer a way out from this innovation ‘valley of death’. This combines systems biology, synthetic biology, bioinformatics and bioprocess development. The project will develop a robust and model-based simulation platform to ramp up the scale-up techniques driven by profound biological and physical understanding. This individual and integrated tool will enable higher level of understanding about the fermentation process, both in the fluid dynamics (physics) and metabolic dynamics (biology), and in their mutual connection. In essence, the project aims to replace conventional, empirical scale-up criteria by predictive modeling and rational design, based on combined biological and physical expertise. The work will focus on two leading industrial cases, comprising the micro-organisms Penicillium chrysogenum and Saccharomyces cerevisiae, but has general implications. The different parts of the platform will be developed by the three academic partners. Then, the platform will be used to assess the Syngulon technology for controlling the phenotypic state of the microbial population, relevant to large-scale performance, based on synthetic biology methods. Finally, DSM as principal end-user of the technology will benefit from the platform to enhance their fermentation process development capability. Development and application of computational approaches to design better scale-down simulators, enable faster scale-up, and improve the energy and resource efficiency of fermentations, will accelerate bringing bio-innovations to the markets. This bio-based drive is further essential to help solving the mega-issues of climate change, food security and energy supply. The integrated computational solution proposed will contribute to reconciliation of two main, competing, business success factors: speed and quality. The project aims at: accelerating the bioprocess development including plant start-up by at least 20% (e.g. from 5 to 4 years), which could after 10 years further develop into a factor 5 (from 5 to 1 year). developing guidelines for constructing strains such that they are robust enough for harsh production conditions the early identification of scale-up sensitive properties of novel producers to ensure efficient use of manpower and research capacities reducing of the order of 10’s of tonnes of CO2 emission per large-scale fermentation reducing the energy requirements of at least 10%, i.e. in the order of 10 MWh, per run executed on industrial scale reducing by at least 20% the development budget (e.g. from 10 M€ to 8 M€), which could after 10 years further advance to a factor 5 (from 10 to 2 M€) * Assuming analogy of key findings of E. coli with S. cerevisiae and P. chrysogenum.
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