Theory of linear and integer programming
Theory of linear and integer programming
Double Description Method Revisited
Selected papers from the 8th Franco-Japanese and 4th Franco-Chinese Conference on Combinatorics and Computer Science
Metatool 5.0: fast and flexible elementary modes analysis
Bioinformatics
Systems Biology: Properties of Reconstructed Networks
Systems Biology: Properties of Reconstructed Networks
A new approach to flux coupling analysis of metabolic networks
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
On Inner and Outer Descriptions of the Steady-State Flux Cone of a Metabolic Network
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Functional similarities of reaction sets in metabolic pathways
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A constraint solver for flexible protein models
Journal of Artificial Intelligence Research
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Metabolic pathway analysis is an important area in computational biology, which has received considerable attention in the recent past. The set of all possible flux distributions over a metabolic network at steady state defines a polyhedral cone, the steady-state flux cone. Two major approaches exist to characterize this cone: elementary flux modes and extreme pathways. Both use an inner description of the flux cone, which is based on sets of generating vectors. The number of these generators may be very large even for small networks. This limits the practical applicability of these methods. We present a new constraint-based approach to metabolic pathway analysis which uses an outer description of the flux cone, based on sets of non-negativity constraints. These can be identified with irreversible reactions and thus have a direct biochemical interpretation. The resulting description of the flux cone is minimal and unique. Furthermore, it satisfies a simplicity condition similar to the one that holds for elementary flux modes.