A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks

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dc.creatorLaubenbacher, Reinhard
dc.creatorStigler, Brandilyn
dc.date2003-12-17
dc.date.accessioned2026-07-07T05:58:04Z
dc.date.available2026-07-07T05:58:04Z
dc.descriptionThis paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. The complexity of the algorithm is quadratic in the number of variables and cubic in the number of time points. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.
dc.description28 pages, 5 EPS figures, uses elsart.cls
dc.identifierhttps://arxiv.org/abs/q-bio/0312026
dc.identifierhttp://arxiv.org/abs/q-bio/0312026
dc.identifierJournal of Theoretical Biology 229 (2004) 523-537
dc.identifierdoi:10.1016/j.jtbi.2004.04.037
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/88221
dc.subjectQuantitative Methods
dc.subjectMolecular Networks
dc.titleA Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
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