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Open Access Highly Accessed Research article

Computing minimal nutrient sets from metabolic networks via linear constraint solving

Steven Eker1, Markus Krummenacker2, Alexander G Shearer2, Ashish Tiwari1, Ingrid M Keseler2, Carolyn Talcott1 and Peter D Karp2*

Author Affiliations

1 Computer Science Laboratory, SRI International, Menlo Park, CA 94025, USA

2 Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA

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BMC Bioinformatics 2013, 14:114  doi:10.1186/1471-2105-14-114

Published: 27 March 2013

Abstract

Background

As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions. One type of phenotypic prediction is to determine sets of compounds that will support the growth of a bacterium from the metabolic network inferred from the genome sequence of that organism.

Results

We present a method for computationally determining alternative growth media for an organism based on its metabolic network and transporter complement. Our method predicted 787 alternative anaerobic minimal nutrient sets for Escherichia coli K–12 MG1655 from the EcoCyc database. The program automatically partitioned the nutrients within these sets into 21 equivalence classes, most of which correspond to compounds serving as sources of carbon, nitrogen, phosphorous, and sulfur, or combinations of these essential elements. The nutrient sets were predicted with 72.5% accuracy as evaluated by comparison with 91 growth experiments. Novel aspects of our approach include (a) exhaustive consideration of all combinations of nutrients rather than assuming that all element sources can substitute for one another(an assumption that can be invalid in general) (b) leveraging the notion of a machinery-duplicating constraint, namely, that all intermediate metabolites used in active reactions must be produced in increasing concentrations to prevent successive dilution from cell division, (c) the use of Satisfiability Modulo Theory solvers rather than Linear Programming solvers, because our approach cannot be formulated as linear programming, (d) the use of Binary Decision Diagrams to produce an efficient implementation.

Conclusions

Our method for generating minimal nutrient sets from the metabolic network and transporters of an organism combines linear constraint solving with binary decision diagrams to efficiently produce solution sets to provided growth problems.

Keywords:
Binary decision diagrams; Computational biology; Linear constraint solving; Minimal nutrient sets; SMT solvers; Metabolic and regulatory networks; Cellular metabolism