Fast Computation of Minimal Cut Sets in Metabolic Networks with a Berge Algorithm that Utilizes Binary Bit Pattern Trees
1. The calculation of all MCSs of an unconstrained system, which means that every determined MCS kills all EFMs of the investigated systems. Numerous applications of this method have been suggested, for example, evaluating structural robustness and fragility, identifying targets in rational drug design, and predicting phenotypes [ 4].
2. EFMs can be utilized to split the functional units of a metabolic system into two groups: a) desired functions and b) unwanted functions [ 5]. A constrained cut set (cCS) is defined as a set of reaction deletions that eliminates all unwanted EFMs and keeps—at least some of—the desired EFMs. Such a strategy can be used to optimize microbiological production hosts to efficiently produce substances of interest, for example, ethanol [ 6]. This is achieved by assigning all EFMs that are involved in efficient ethanol production to the set of desired EFMs. All other EFMs are assigned to the set of unwanted EFMs. Consequently, the biological implementation of the computed MCSs of such a system results in an optimized microorganism. Therefore, MCSs are a valuable tool to identify and realize optimal gene intervention strategies, as has been shown by Trinh et al. [ 7].
1. the set of all EFMs to be killed,
2. the set of preminimal cut sets (preMCS), which kill all EFMs that have already been processed, and
3. the new preMCS candidates.
1. eliminating reactions that must not be knocked out, as this would result in the deletion of (too many) wanted modes,
2. removing duplicate modes,
3. removing modes that are supersets of other modes, and
4. combining duplicate reactions.
Table 2. Comparison of MCS Computation Runs with (a) Linear Search, (b) Tree Search with Random Bitorder, and (c) Tree Search with a Bit Order Derived from EFM Properties for a System with 114,614 EFMs
C. Jungreuthmayer and J. Zanghellini are with the Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, and with the Metabolic Modeling Group, Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11/DG, 1190 Vienna, Austria. E-mail: email@example.com.
M. Beurton-Aimar is with the Laboratoire Bordelais de Recherche en Informatique (LABRI), University of Bordeaux, 351, cours de la Liberation, Batiment A30, Bureau 310, Talence 33400, France.
Manuscript received 3 Apr. 2013; revised 9 July 2013; accepted 4 Sept. 2013; published online 18 Sept. 2013.
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Digital Object Identifier no. 10.1109/TCBB.2013.116.