Robust intervention in probabilistic boolean networks

Ranadip Pal, Aniruddha Datta, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Probabilistic Boolean Networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. To date, a number of intervention strategies have been proposed in the context of Probabilistic Boolean networks. However, all these techniques assume perfect knowledge of the transition probability matrix of the PBN. Such an assumption cannot be satisfied in practice since the presence of noise and the availability of limited number of samples will prevent the transition probabilities from being accurately determined. Moreover, even if the exact transition probabilities could be estimated from the data, mismatch between the PBN model and the actual genetic regulatory network will invariably be present. In this paper, we develop a robust intervention strategy that is obtained by minimizing the worst-case cost over the uncertainties in the entries of the transition probability matrix.

Original languageEnglish
Title of host publicationProceedings of the 2007 American Control Conference, ACC
Number of pages6
StatePublished - 2007
Event2007 American Control Conference, ACC - New York, NY, United States
Duration: Jul 9 2007Jul 13 2007

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2007 American Control Conference, ACC
Country/TerritoryUnited States
CityNew York, NY


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