Synthesizing Boolean networks with a given attractor structure

Ranadip Pal, Ivan Ivanov, Aniruddha Datta, Michael L. Bittner, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). The long-run behavior of a BN is characterized by its attractors. We derive two algorithms for the attractor inverse problem where the attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion has been used to test a designed Probabilistic Boolean Network (PBN) constructed from melanoma gene-expression data.

Original languageEnglish
Title of host publication2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006
Pages73-74
Number of pages2
DOIs
StatePublished - 2006
Event2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006 - College Station, TX, United States
Duration: May 28 2006May 30 2006

Publication series

Name2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006

Conference

Conference2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006
CountryUnited States
CityCollege Station, TX
Period05/28/0605/30/06

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    Pal, R., Ivanov, I., Datta, A., Bittner, M. L., & Dougherty, E. R. (2006). Synthesizing Boolean networks with a given attractor structure. In 2006 IEEE International Workshop on Genomic Signal Processing and Statstics, GENSIPS 2006 (pp. 73-74). [4161783] (2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006). https://doi.org/10.1109/GENSIPS.2006.353162