An efficient graph theory based method to identify every minimal reaction set in a metabolic network

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dc.contributor.author Jonnalagadda, Sudhakar
dc.contributor.author Srinivasan, Rajagopalan
dc.date.accessioned 2014-03-18T18:48:39Z
dc.date.available 2014-03-18T18:48:39Z
dc.date.issued 2014-03
dc.identifier.citation Jonnalagadda, Sudhakar and Srinivasan, Rajagopalan, "An efficient graph theory based method to identify every minimal reaction set in a metabolic network", BMC Systems Biology, DOI: 10.1186/1752-0509-8-28, vol. 8, pp. 28, Mar. 2014. en_US
dc.identifier.issn 1752-0509
dc.identifier.uri http://dx.doi.org/10.1186/1752-0509-8-28
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/925
dc.description.abstract Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. en_US
dc.description.statementofresponsibility by Sudhakar Jonnalagadda and Rajagopalan Srinivasan
dc.format.extent Vol. 8, pp. 28
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.subject Genome-scale en_US
dc.subject Graph theory en_US
dc.subject Metabolic network en_US
dc.title An efficient graph theory based method to identify every minimal reaction set in a metabolic network en_US
dc.type Article en_US
dc.relation.journal BMC Systems Biology


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