BIODYNAMICS approach will aim to demonstrate that multipurpose microbial cell factories based on a standardized, modular plug-andplay synthetic biology pipeline are a real possibility for quicker, cheaper and more effective bioproduction. To this end, we will develop novel hybrid (data-driven and mechanistic) modelling and characterization methods and tools for advanced metabolic engineering biology. In particular, it will consider the design, implementation and analysis of optimal synthetic dynamic feedback regulation mechanisms in de novo metabolic pathways for the production of metabolites of interest in microbial cell factories. It will focus on context-aware methods that consider allocation of cell resources and its interplay with cell growth and environmental changes that occur in a bioreactor setup where even a community of diverse symbiotic microorganisms may co-exist. It will also consider the use of metabolic extended biosensors providing sensing capabilities beyond natural effectors to extend the effective working range of feedback bio-controllers. To overcome some of the current limitations to implement complex synthetic gene devices, BIODYNAMICS will develop machine learning and optimisation tools for functional context-aware standard characterisation of bioparts and modules. These will be integrated within a closed loop DBTL workflow using low cost open-source hardware and software, thus facilitating model-guided combinational biosynthesis for automated modular pathway design and implementation. BIODYNAMICS will validate its methods and tools using relevant case studies, including the production of phenylpropanoids of industrial interest using the bacterial host E. coli. To achieve these objectives, BIODYNAMICS integrates two research groups with long and renowned experience. The group SB2CLab- UPV brings in experience in bioprocess control systems, synbio for dynamic regulation and bioinformatics tools for metabolic engineering, along with experience in process automation and machine learning methods, and expertise in experimental molecular biology lab work. The group IIM-CSIC brings in a long and renowned experience in bioprocess model-building (reverse engineering, network inference, nonlinear system identification) and optimization (including global and multi-criteria dynamic optimization) in the areas of systems and synthetic biology.

We reinforce our expertise with the cooperation of external well-known researchers: Prof. G.-B. Stan (Imperial College London, UK), an expert in systems and control engineering applied to synbio, Prof. H. De Battista (CONICET, Argentine), in sliding mode techniques applied to biosystems, and Dr. Jacob Beal (Raytheon BBN Technologies, US), in automation and standardisation for synbio and leader of the Synthetic Biology Open Languaje team. Prof. J. Saez-Rodriguez (Heideberg University), an expert in modelling of complex biochemical pathways, and Prof. Sebastian Sager (Otto-von-Guericke University Magdeburg), an expert in mathematical optimization. In addition, two leader biotech companies will contribute to the relevance and exploitation of results: ADM-Biopolis, with core expertise in designing and developing microorganisms for industrial and health-related purposes and SilicoLife, that designs optimized microorganisms and novel pathways for industrial biotechnology applications, based on computational metabolic engineering and synbio approaches.

Programme: SB2CLab


Funding codes:
  • PID2020-117271RB-C21
  • MCIN/AEI/10.13039/501100011033

Public web page: Not specified

Organisms: No Organisms specified

FAIRDOM PALs: No PALs for this Project

Project start date: 1st Sep 2021

Project end date: 31st Aug 2024

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