NDLI: Revealing temporal genetic regulatory networks from steady-state distributions

Content Provider	IEEE Xplore Digital Library
Author	Martins, D.C. de Oliveira, E.A. Silva, P. Hashimoto, R.F. Cesar, R.M.
Copyright Year	2009
Description	Author affiliation: Instituto de Matemática e Estatística, Universidade de São Paulo - Brazil (Martins, D.C.; de Oliveira, E.A.; Silva, P.; Hashimoto, R.F.; Cesar, R.M.)
Abstract	The design of dynamical networks from steady-state distributions usually presents some inherent limitations. The dynamical behavior of the system can not be determined from the steady-state, it can only be constrained by it. In general, there is a huge number of dynamical systems that can produce the same steady-state. Nevertheless, it is possible to further constrain the possibilities by adopting the Probabilistic Genetic Networks model which is based on axioms that usually make sense in biological systems. In this work we introduce a new method for the inference of dynamical systems, and their underlying logical structures, from a steady-state distribution. Our method is based on the assumption that biological systems are quasi-deterministic. The technique is based on an integer programming model that selects stochastic matrices with a known limit distribution. These transition matrices reveal how the dynamical system evolves, allowing the application of standard inference methods to discover dependencies among elements of the system.
Starting Page	1
Ending Page	4
File Size	130122
Page Count	4
File Format	PDF
ISBN	9781424447619
DOI	10.1109/GENSIPS.2009.5174335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Stochastic systems Stochastic processes Biological systems Genetics Linear programming Steady-state Gene expression Bioinformatics
Content Type	Text
Resource Type	Article

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