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A CoD-based stationary control policy for intervening in large gene regulatory networks

BACKGROUND: One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burd...

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Detalles Bibliográficos
Autores principales: Ghaffari, Noushin, Ivanov, Ivan, Qian, Xiaoning, Dougherty, Edward R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236832/
https://www.ncbi.nlm.nih.gov/pubmed/22165980
http://dx.doi.org/10.1186/1471-2105-12-S10-S10
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author Ghaffari, Noushin
Ivanov, Ivan
Qian, Xiaoning
Dougherty, Edward R
author_facet Ghaffari, Noushin
Ivanov, Ivan
Qian, Xiaoning
Dougherty, Edward R
author_sort Ghaffari, Noushin
collection PubMed
description BACKGROUND: One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network. RESULTS: In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model. CONCLUSIONS: The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.
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spelling pubmed-32368322011-12-14 A CoD-based stationary control policy for intervening in large gene regulatory networks Ghaffari, Noushin Ivanov, Ivan Qian, Xiaoning Dougherty, Edward R BMC Bioinformatics Proceedings BACKGROUND: One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network. RESULTS: In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model. CONCLUSIONS: The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy. BioMed Central 2011-10-18 /pmc/articles/PMC3236832/ /pubmed/22165980 http://dx.doi.org/10.1186/1471-2105-12-S10-S10 Text en Copyright ©2011 Ghaffari et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Ghaffari, Noushin
Ivanov, Ivan
Qian, Xiaoning
Dougherty, Edward R
A CoD-based stationary control policy for intervening in large gene regulatory networks
title A CoD-based stationary control policy for intervening in large gene regulatory networks
title_full A CoD-based stationary control policy for intervening in large gene regulatory networks
title_fullStr A CoD-based stationary control policy for intervening in large gene regulatory networks
title_full_unstemmed A CoD-based stationary control policy for intervening in large gene regulatory networks
title_short A CoD-based stationary control policy for intervening in large gene regulatory networks
title_sort cod-based stationary control policy for intervening in large gene regulatory networks
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236832/
https://www.ncbi.nlm.nih.gov/pubmed/22165980
http://dx.doi.org/10.1186/1471-2105-12-S10-S10
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