Optimizing the metabolism of microorganisms for maximum yields and titers is a critical step in improving bioprocess economics. The challenges in developing the sustainable production of bio-based chemicals is understanding and controlling the underlying mechanisms that govern the stability and performance of engineered biological systems. Our aim is to optimize relevant regulatory and metabolic networks of itaconic production of Yarrowia lipolytica by integrating machine learning methods and multi-omics measurements to characterize the host response to engineered biological functions with ultimate aim to predict the flux control coefficients for the best-performing itaconate-producing strain. In this regard, we build on recent advances in the scalability of Bayesian Inference methods to enable the quantification of uncertainty for a variety of key parameters needed for metabolic control analysis. We demonstrate using central metabolism of Yarrowia lipolytica evidence that metabolic control analysis can readily improve microbial cell factories for biofuels production. This in turn can be used for future strain design and subsequent engineering applications.

Bayesian inference and metabolic control analysis for multi-omics data integration

ACS Spring Meeting 2021

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