BIODYNAMICS approach will aim to demonstrate that multipurpose microbial cell factories based on a standardized, modular plug-and-play synthetic biology pipeline are a real possibility for quicker, cheaper and more effective bioproduction.

To this end, researchers of the Instituto ai2-UPV are developing novel hybrid (data-driven and mechanistic) modeling and characterization methods and tools for advanced metabolic engineering biology. In particular, this project 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 optimization tools for functional context-aware standard characterization of bioparts and modules.

To achieve these objectives, the group SB2CLab-UPV brings in a long and renowned 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. They reinforce their expertise with the cooperation of three external wellknown researchers from the Imperial College London (UK), CONICET (Argentina), and Raytheon BBN Technologies (US). In addition, two leader biotech companies will contribute to the relevance and exploitation of results: ADM-Biopolis and SilicoLife.