@article{470b48225a6a4cc5976c7589e8821a84,
title = "Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms",
abstract = "Understanding the governing principles behind organisms{\textquoteright} 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.",
author = "Suthers, {Patrick F.} and Foster, {Charles J.} and Debolina Sarkar and Lin Wang and Maranas, {Costas D.}",
note = "Funding Information: Funding provided by The Center for Bioenergy Innovation a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. This work was partially funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018420). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Funding also provided by the DOE Office of Science, Office of Biological and Environmental Research (Award Number DE-SC0018260) and NSF Award Number MCB-1615646. Funding Information: Funding provided by The Center for Bioenergy Innovation a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science . This work was partially funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation ( U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018420 ). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Funding also provided by the DOE Office of Science, Office of Biological and Environmental Research (Award Number DE-SC0018260 ) and NSF Award Number MCB-1615646 . Publisher Copyright: {\textcopyright} 2020 International Metabolic Engineering Society",
year = "2021",
month = jan,
doi = "10.1016/j.ymben.2020.11.013",
language = "English (US)",
volume = "63",
pages = "13--33",
journal = "Metabolic Engineering",
issn = "1096-7176",
publisher = "Academic Press Inc.",
}