Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a mathematical technique that estimates intracellular metabolic fluxes for complex biological systems.iMFA software uses experimental data (extracellular fluxes, isotope labeling patterns) and a curated network model as inputs and produces a quantitative metabolic flux map as the output.iMFA determines a set of flux values that produce the best match between the simulated and experimental mass distribution vectors using an iterative optimization process.iMFA is essential for the metabolic analysis of complex systems and enables the discovery of new metabolic pathways.Advances in iMFA are needed to enable analysis of dynamic metabolic states, multicellular co-cultures, and genome-scale networks. These new tools will significantly advance our understanding of metabolic complexity. Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.