Title |
Steady state analysis of Boolean molecular network models via model reduction and computational algebra
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Published in |
BMC Bioinformatics, June 2014
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DOI | 10.1186/1471-2105-15-221 |
Pubmed ID | |
Authors |
Alan Veliz-Cuba, Boris Aguilar, Franziska Hinkelmann, Reinhard Laubenbacher |
Abstract |
A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general. |
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