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Dynamical modeling of drug effect using hybrid systems

Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynam...

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Detalles Bibliográficos
Autores principales: Li, Xiangfang, Qian, Lijun, Dougherty, Edward R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639233/
https://www.ncbi.nlm.nih.gov/pubmed/23268741
http://dx.doi.org/10.1186/1687-4153-2012-19
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author Li, Xiangfang
Qian, Lijun
Dougherty, Edward R
author_facet Li, Xiangfang
Qian, Lijun
Dougherty, Edward R
author_sort Li, Xiangfang
collection PubMed
description Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results.
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spelling pubmed-36392332013-05-06 Dynamical modeling of drug effect using hybrid systems Li, Xiangfang Qian, Lijun Dougherty, Edward R EURASIP J Bioinform Syst Biol Research Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results. BioMed Central 2012 2012-12-26 /pmc/articles/PMC3639233/ /pubmed/23268741 http://dx.doi.org/10.1186/1687-4153-2012-19 Text en Copyright © 2012 Li et al.; licensee Springer. 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 Research
Li, Xiangfang
Qian, Lijun
Dougherty, Edward R
Dynamical modeling of drug effect using hybrid systems
title Dynamical modeling of drug effect using hybrid systems
title_full Dynamical modeling of drug effect using hybrid systems
title_fullStr Dynamical modeling of drug effect using hybrid systems
title_full_unstemmed Dynamical modeling of drug effect using hybrid systems
title_short Dynamical modeling of drug effect using hybrid systems
title_sort dynamical modeling of drug effect using hybrid systems
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639233/
https://www.ncbi.nlm.nih.gov/pubmed/23268741
http://dx.doi.org/10.1186/1687-4153-2012-19
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