BiosInt: Biosensor-based smart design of pathway dynamic regulation for industrial biomanufacturing

Industrial-scale production of bio-based chemicals in the circular green bioeconomy still faces inefficiencies arising from scaling up challenges. Biosensor mediated control of metabolic pathways has been proposed as a strategy to improve the performance of those bioprocesses. By linking industrial biosynthetic pathways to measurable metabolites through biochemical transformations, a highly interconnected regulatory design space is unveiled, offering new opportunities for pathway dynamic control. Yet, a systematic approach for selecting and implementing genetic circuits within such large space has remained absent. Here, we introduce BiosInt, an allosteric transcription factor-based genetic biocircuit, with quasi-integral adaptation control capabilities that can be used to increase robustness and performance of biomanufacturing engineered strains. To test the capabilities of the circuit, we have analysed its performance for the full set of metabolic pathway topologies found in the bio-based chemical production space of compounds with industrial interest. For a given implementation of the BiosInt circuit, we carried out a multiobjective optimization process that provides the inverse control solution of optimal topology starting from any given pathway configuration and enzyme expression ratios. Next, synthetic datasets are generated to train machine learning-based predictive models with the data obtained from the simulations of each topology by varying enzyme expression levels and their corresponding concentrations. We validated the models by showing their ability to predict the best control topology for a given set of enzyme ratios. As a proof-of-concept, we show its application to the design of the genetic constructs expressing flavonoid production pathways, providing optimal performance for the BiosInt-mediated dynamic regulation. This circuit inverse design and its application to dynamic control in biomanufacturing paves the way for a future design pipeline in biofoundries delivering more robust and efficient sustainable bioproduction processes.