Abstract
BackgroundAs 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.ResultsWe 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.ConclusionsOur 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.
Highlights
As more complete genome sequences become available, bioinformatics challenges arise in how to exploit genome sequences to make phenotypic predictions
A model of the Escherichia coli metabolic network could be “fed” the constituent compounds of M9 minimal medium, and the expectation would be that all the biomass compounds should be present in the final, fixed set of compounds generated via forward propagation
The E. coli constraint-based model This section describes the inputs we provided to the minimal nutrient prediction algorithm to compute the minimal nutrients of E. coli under anaerobic conditions
Summary
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. Given the high cost of evaluating laboratory growth conditions and the relative abundance of powerful genome sequencing resources, it makes sense to ask whether we can use the metabolic network inferred from an organism’s genome sequence to predict the media that will support the growth of the organism. We have shown that the completeness of a metabolic network can be evaluated using a “forward propagation” approach [6] This purely qualitative modeling approach treats each reaction as a rule that will “fire” if all of its reactants are present. A model of the Escherichia coli metabolic network could be “fed” the constituent compounds of M9 minimal medium, and the expectation would be that all the biomass compounds should be present in the final, fixed set of compounds generated via forward propagation
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