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Identification of control targets in Boolean molecular network models via computational algebra

BACKGROUND: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a...

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Autores principales: Murrugarra, David, Veliz-Cuba, Alan, Aguilar, Boris, Laubenbacher, Reinhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035508/
https://www.ncbi.nlm.nih.gov/pubmed/27662842
http://dx.doi.org/10.1186/s12918-016-0332-x
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author Murrugarra, David
Veliz-Cuba, Alan
Aguilar, Boris
Laubenbacher, Reinhard
author_facet Murrugarra, David
Veliz-Cuba, Alan
Aguilar, Boris
Laubenbacher, Reinhard
author_sort Murrugarra, David
collection PubMed
description BACKGROUND: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. RESULTS: This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg. CONCLUSIONS: This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0332-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-50355082016-09-29 Identification of control targets in Boolean molecular network models via computational algebra Murrugarra, David Veliz-Cuba, Alan Aguilar, Boris Laubenbacher, Reinhard BMC Syst Biol Research Article BACKGROUND: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. RESULTS: This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg. CONCLUSIONS: This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0332-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-23 /pmc/articles/PMC5035508/ /pubmed/27662842 http://dx.doi.org/10.1186/s12918-016-0332-x Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Murrugarra, David
Veliz-Cuba, Alan
Aguilar, Boris
Laubenbacher, Reinhard
Identification of control targets in Boolean molecular network models via computational algebra
title Identification of control targets in Boolean molecular network models via computational algebra
title_full Identification of control targets in Boolean molecular network models via computational algebra
title_fullStr Identification of control targets in Boolean molecular network models via computational algebra
title_full_unstemmed Identification of control targets in Boolean molecular network models via computational algebra
title_short Identification of control targets in Boolean molecular network models via computational algebra
title_sort identification of control targets in boolean molecular network models via computational algebra
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035508/
https://www.ncbi.nlm.nih.gov/pubmed/27662842
http://dx.doi.org/10.1186/s12918-016-0332-x
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