Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling

Andrew McNaughton, Erin Bredeweg, James Manzer, Jeremy Zucker, Nathalie Munoz, Meagan Burnet, Ernesto Nakayasu, Kyle Pomraning, Eric Merkley, Ziyu Dai, William Chrisler, Scott Baker, Peter St. John, Neeraj Kumar

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

Abstract

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.
Original languageAmerican English
Pages (from-to)2968-2981
Number of pages14
JournalACS Synthetic Biology
Volume10
Issue number11
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-2700-81051

Keywords

  • Bayesian inference
  • data integration
  • itaconate
  • metabolic control analysis
  • metabolic modeling
  • multiomics
  • strain design
  • Yarrowia lipolytica

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