Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

TL;DR

The paper introduces COBRA Toolbox v3.0, a comprehensive software suite for constraint-based modeling of biochemical networks, featuring new methods for reconstruction, analysis, visualization, and integration of multi-omics data, expanding its application scope.

Contribution

It presents an updated version of the COBRA Toolbox with advanced methods and multi-lingual code integration, enabling broader and more precise biochemical network analysis.

Findings

Enhanced reconstruction and analysis capabilities

Integration of multi-omics data for comprehensive modeling

Support for high-precision, nonlinear optimization

Abstract

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.