Abstract
BackgroundConstraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement.ResultsHere we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways.ConclusionsApplying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.
Highlights
Constraint-based analysis has become a widely used method to study metabolic networks
In the Methods section we develop the underlying mixed-integer linear programming (MILP) methods
Basic MILP to compute a minimum subnetwork We always assume that our network is in steady-state, i.e., Sv = 0, with bounds on the reaction rates l ≤ v ≤ u
Summary
Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. Genome-scale metabolic network reconstructions are used to build in silico models of cellular metabolism [1] To analyze these models, a large variety of constraint-based methods has been developed over the years [2]. The metabolic network is assumed to be in steady-state, i.e., the production and consumption of the internal metabolites has to be balanced This leads to a flux space of the form C = {v ∈ RRxn | Sv = 0, l ≤ v ≤ u}. A natural question is whether it is possible to
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