Systematic evaluation of extraction and simulation methods for development of high-quality cancer patient-specific metabolic models

Abstract

With the advent of generic human genome-scale metabolic models (GEM), they have been increasingly applied to a range of diseases, especially cancers. When studying cancer metabolism using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patientspecific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy, two MSMs commonly used for studying cancer, flux balance analysis (FBA) and parsimonious FBA (pFBA) showed relatively poorer performance than other MSMs including least absolute deviation (LAD). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.