PYTHON AS A TOOL FOR GENOME-SCALE METABOLIC MODEL RECONSTRUCTION AND ANALYSIS

Abstract

The cost reduction of high-throughput technologies has led to the development of data analysis methods that deal with the increasing availability of omics datasets. Constraint-based genome-scale metabolic modeling is an example of such a method. It uses omics data and biochemical networks, a mechanistic framework to combine omics data, to describe steady-state metabolic flux distributions. Therefore, a system of mass balance equations is created that can be solved via linear programming. To date, most software for constraint-based genome-scale modeling has relied on MATLAB, a popular proprietary programming language. However, Matlab has issues when dealing with the integration of omics datasets and modeling complex biological processes beyond metabolism. In contrast, Python, an object-oriented open-source programming language, is a better choice for solving these issues. It has a relatively easy syntax, offers an extensive community, and allows seamless integration of omics datasets. Owing to these factors, Python is a good emerging tool for constraint-based genome-scale metabolic modeling. This review aims to provide an introduction and systematic overview of Python-based tools for constraint-based genome-scale metabolic modeling. We installed, checked the documentation, and tried to use each tool described in the review. We grouped each tool into one of the three categories (GEM reconstruction and maintenance, phenotype simulation, or visualization software) and provided detailed tool and concept explanations when necessary.