Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms.

Journal: Metabolic engineering
Published Date: Dec 10, 2020

Abstract

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Authors

  • Patrick F Suthers
    Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
  • Charles J Foster
    Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
  • Debolina Sarkar
    Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Costas D Maranas
    Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA. Electronic address: costas@psu.edu.

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