Abstract
BackgroundDevelopment 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.ResultsIn 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.ConclusionsIdentification 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.
Highlights
Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels
Case Study 1: Aerobic growth of Escherichia coli on glucose
The network contains 63 metabolites and 77 reactions [29]. These 77 reactions are first grouped based on dependency as described in Reaction dependency and grouping
Summary
Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Synthetic biology provides the tools and techniques to design and construct artificial cells with minimal functionality containing a minimal genome, but with all the essential genes for survival in a defined environment and possessing replication capabilities [8]. Such minimal cells provide a platform for efficient production of desired chemicals and decontamination of waste streams [9,10]. Their study finds that minimal reaction sets are strongly dependent on medium constituents and cellular objectives This approach does not provide any indication on what reactions have to be blocked in order to construct the cell with minimal metabolism besides its computational complexity is high. Significant reduction in the computational time has been achieved using the graph theory based approach compared to classical math programming
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