dorsal/arxiv
View SchemaA Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
| Authors | Reinhard Laubenbacher, Brandilyn Stigler |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0312026 |
| URL | https://arxiv.org/abs/q-bio/0312026 |
| DOI | 10.1016/j.jtbi.2004.04.037 |
| Journal | Journal of Theoretical Biology 229 (2004) 523-537 |
Abstract
This 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.
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"abstract": "This paper proposes a new method to reverse engineer gene regulatory networks\nfrom experimental data. The modeling framework used is time-discrete\ndeterministic dynamical systems, with a finite set of states for each of the\nvariables. The simplest examples of such models are Boolean networks, in which\nvariables have only two possible states. The use of a larger number of possible\nstates allows a finer discretization of experimental data and more than one\npossible mode of action for the variables, depending on threshold values.\nFurthermore, with a suitable choice of state set, one can employ powerful tools\nfrom computational algebra, that underlie the reverse-engineering algorithm,\navoiding costly enumeration strategies. To perform well, the algorithm requires\nwildtype together with perturbation time courses. This makes it suitable for\nsmall to meso-scale networks rather than networks on a genome-wide scale. The\ncomplexity of the algorithm is quadratic in the number of variables and cubic\nin the number of time points. The algorithm is validated on a recently\npublished Boolean network model of segment polarity development in Drosophila\nmelanogaster.",
"arxiv_id": "q-bio/0312026",
"authors": [
"Reinhard Laubenbacher",
"Brandilyn Stigler"
],
"categories": [
"q-bio.QM",
"q-bio.MN"
],
"doi": "10.1016/j.jtbi.2004.04.037",
"journal_ref": "Journal of Theoretical Biology 229 (2004) 523-537",
"title": "A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks",
"url": "https://arxiv.org/abs/q-bio/0312026"
},
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