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.

了解生物体新陈代谢和生长背后的管理原则是将它们有效部署为生物生产底盘的基础。代谢建模的一个核心目标是预测代谢和生长如何受到外部环境因素和内部基因型扰动的影响。反应化学计量学、热力学和质量作用动力学的基本概念已经成为许多建模框架的基本原理，这些框架旨在描述生物如何以及为什么将资源分配给生长和生物生产。本综述重点关注最新的算法进步，这些进步将这些基本原则整合到日益复杂的量化框架中。