Multiomics-Driven Microbial Model Optimization
Authors:
Janis Shin (jshin1@uw.edu)1*, Herbert M. Sauro1, Jeremy Zucker2, and James Carothers1
Institutions:
1University of Washington; and 2Pacific Northwest National Laboratory
URLs:
Goals
The project’s goal is to create genome-scale models of endogenous metabolic pathways and develop metabolic sensitivity maps to identify reactions that dominate the control of flux. Specifically, the teamaims to gain insight into the regulatory mechanisms within pathways and predict outcomes of metabolic interventions at the genome scale.
Abstract
Biomanufacturing poses a sustainable approach to wean humanity’s reliance on petrochemical-derived commodity products. Despite the advent of omics data and genome-scale models, there is no straightforward process for integrating all this data to design biochemical pathways that produce chemicals at an industrial scale. To understand and engineer metabolism, researchers must identify which enzymes exert the most influence on metabolite concentrations and fluxes through the biochemical pathway. Theoretical work also suggests that the cast of enzymes exerting control over the pathway changes under different growth conditions. To identify these influential enzymes, steady state enzyme perturbation data is used within a genome-scale context containing multiple metabolic engineering interventions. Researchers approximate Michaelis-Menten kinetics near the reference steady state through a lin-log model and supply these calculations to a Bayesian inference model. The inference model estimates each reaction’s influence on the metabolic pathway and thus provides metabolic intervention targets for improving bioproduction titers and rates. This method was successfully applied to estimate sensitivities in yeast metabolism (St. John et al. 2019; McNaughton et al. 2021). This poster will present the extension of this approach to Pseudomonas putida.
References
McNaughton, A. D., et al. 2021. “Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling,” ACS Synthetic Biology, 10(11), 2968–81.
St. John, P. C., et al. 2019. “Bayesian Inference of Metabolic Kinetics from Genome-Scale Multiomics Data,” PLoS Computational Biology 15(11), e1007424.
Funding Information
This research was supported by the U.S. Department of Energy Bioenergy Technologies Office (BETO), grant no. DE-EE0008927 and DOE Office of Science, Biological and Environmental Research (BER) Program, under Funding Opportunity Announcement DE-FOA-0002600.