The COnstraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies.    

The openCOBRA project has arisen to provide researchers with easy access to core COBRA methodologies, and to provide a repository for community contributed modules that build off of these core COBRA Features.  The openCOBRA Project was initiated with tools for MATLAB but has now grown to include Python-based modules for dealing with the complex relationships of the next generation of COBRA models.

The openCOBRA project is currently maintained by Daniel R. Hyduke from the University of California – San Diego and Ronan M. T. Fleming from the University of Iceland.

This project exists because of the past endeavors of interesting individuals and is maintained as an extracurricular activity by people from Systems Biology of EnteroPathogens funded by the National Institute of Allergy and Infectious Diseases NIH/DHHS through interagency agreement Y1-AI-8401-01 and the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program grant DE-SC0002009.