Abstract
BackgroundKnockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.ResultsTo illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock.ConclusionsPSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
Highlights
Knockout strategies, the concept of constrained minimal cut sets, are an important part of the arsenal of tools used in manipulating metabolic networks
In [8], Constrained minimal cut set (cMCS) are calculated by first solving a series of mixed integer linear programming Mixed integer linear programming (MILP) problems representing (1) and (3) and filtering those solutions which satisfy (4)
To clarify the working of MCS software package based on PSO (PSOMCS), we first apply our method to a small toy network, optimizing for only a single reaction
Summary
The concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. CMCSs can be calculated even in genome-scale networks. In a recent work by Ballerstein et al, it was shown that cMCS can be directly calculated from the stoichiometric matrix [7]. Using this method, it is possible to calculate intervention strategies even in genome-scale metabolic networks [8]. It is possible to calculate intervention strategies even in genome-scale metabolic networks [8] Another work extended this concept to include regulation [9].
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