{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T12:23:40Z","timestamp":1706185420839},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"24","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,12,15]]},"abstract":"Abstract<\/jats:title>\n Motivation: A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their long-run behavior using Markov chain theory. The long-run dynamics of a PBN, as represented by its steady-state distribution (SSD), can guide the design of effective intervention strategies for the modeled systems. A major obstacle for its application is the large state space of the underlying Markov chain, which poses a serious computational challenge. Hence, it is critical to reduce the model complexity of PBNs for practical applications.<\/jats:p>\n Results: We propose a strategy to reduce the state space of the underlying Markov chain of a PBN based on a criterion that the reduction least distorts the proportional change of stationary masses for critical states, for instance, the network attractors. In comparison to previous reduction methods, we reduce the state space directly, without deleting genes. We then derive stationary control policies on the reduced network that can be naturally induced back to the original network. Computational experiments study the effects of the reduction on model complexity and the performance of designed control policies which is measured by the shift of stationary mass away from undesirable states, those associated with undesirable phenotypes. We consider randomly generated networks as well as a 17-gene gastrointestinal cancer network, which, if not reduced, has a 217 \u00d7 217 transition probability matrix. Such a dimension is too large for direct application of many previously proposed PBN intervention strategies.<\/jats:p>\n Contact: \u00a0xqian@cse.usf.edu<\/jats:p>\n Supplementary information: \u00a0Supplementary information are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq575","type":"journal-article","created":{"date-parts":[[2010,12,1]],"date-time":"2010-12-01T08:08:45Z","timestamp":1291190925000},"page":"3098-3104","source":"Crossref","is-referenced-by-count":28,"title":["State reduction for network intervention in probabilistic Boolean networks"],"prefix":"10.1093","volume":"26","author":[{"given":"Xiaoning","family":"Qian","sequence":"first","affiliation":[{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77230, USA"}]},{"given":"Noushin","family":"Ghaffari","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77230, USA"}]},{"given":"Ivan","family":"Ivanov","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77230, USA"}]},{"given":"Edward R.","family":"Dougherty","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77230, USA"},{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. Anderson Cancer Center, Houston, TX 77230, USA"},{"name":"1 Department of Computer Science & Engineering, University of South Florida, Tampa, FL 33620, 2Department of Electrical & Computer Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, 4Translational Genomics Research Institute, 400 N 5th Street, Suite 1600, Phoenix, AZ 85004 and 5Department of Bioinformatics & Computational Biology, University of Texas M. D. 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