Multiscale Modeling of Complex Metabolic and Regulatory Networks of Yarrowia lipolytica
EMSL Project ID
51198
Abstract
Modifying genetic and metabolic pathways in microbes/plants to design systems with desired function remains challenging. Our inability to anticipate how the host organism will respond is due to the many fundamental gaps that exist between a molecular-level analysis and a cellular-level understanding of metabolism essential to uncover interactions and dynamics of complex microbial systems. The goal of this project is to develop a multiscale computational framework integrating machine learning, thermokinetic metabolic modeling and multi-omics measurements to characterize the host response to engineered biological functions in terms of a loss function that can be optimized to generate actionable insights for improved designs. The proposed research will apply to the rational design of eukaryotic microbes, specifically the yeast Yarrowia lipolytica for the production of bio-products by computationally optimizing relevant regulatory and metabolic networks at the molecular level as a way to screen a wide landscape of options without requiring laborious manual experiments. By genetically perturbing the engineered organism and quantifying the effect of each perturbation using multi-omics measurements, we will leverage advances in machine learning and causal inference to identify which genes must be modulated to improve the thermodynamic favorability desired metabolic pathway to improve titer, rate and yield.
Project Details
Start Date
2019-10-23
End Date
2020-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members
Related Publications
Shane R. Canon, Christopher S. Henry, Rajendra P. Joshi, Neeraj Kumar, Lee Ann McCue, Andrew McNaughton, Dennis G. Thomas. 2021. "Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions." ACS Omega https://doi.org/10.1021/acsomega.1c00997