MCS2: Minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks

Date created: 
2019-08-21
Identifier: 
etd20604
Keywords: 
Metabolic network models
Minimal cut sets
Drug target identification
Linear programming duality
Mixed Integer Linear Programming
Abstract: 

Constraint-based modeling of metabolic networks helps researchers gain insight into the metabolic processes of many organisms, both prokaryotic and eukaryotic. Minimal Cut Sets (MCSs) are minimal sets of reactions whose inhibition blocks a target reaction in a metabolic network. Most approaches for finding the MCSs in constrained-based models require, either as an intermediate step or as a byproduct of the calculation, the computation of the set of elementary flux modes (EFMs), a convex basis for the valid flux vectors in the network. Recently, Ballerstein et al. proposed a method for computing the MCSs of a network without first computing its EFMs, by creating a dual network whose EFMs are a superset of the MCSs of the original network. However, their dual network is always larger than the original network and depends on the target reaction. Here we propose the construction of a different dual network, which is typically smaller than the original network and is independent of the target reaction, for the same purpose. We prove the correctness of our approach, MCS2, and describe how it can be modified to compute the few smallest MCSs for a given target reaction.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
Leonid Chindelevitch
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.
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