An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

Handle URI:
http://hdl.handle.net/10754/564347
Title:
An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems
Authors:
Kuwahara, Hiroyuki; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Gene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC); Structural and Functional Bioinformatics Group
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 9th International Conference on Computational Methods in Systems Biology - CMSB '11
Conference/Event name:
9th International Conference on Computational Methods in Systems Biology, CMSB'11
Issue Date:
2011
DOI:
10.1145/2037509.2037522
Type:
Conference Paper
ISBN:
9781450308175
Appears in Collections:
Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-04T06:24:26Zen
dc.date.available2015-08-04T06:24:26Zen
dc.date.issued2011en
dc.identifier.isbn9781450308175en
dc.identifier.doi10.1145/2037509.2037522en
dc.identifier.urihttp://hdl.handle.net/10754/564347en
dc.description.abstractGene therapy has a great potential to become an effective treatment for a wide variety of diseases. One of the main challenges to make gene therapy practical in clinical settings is the development of efficient and safe mechanisms to deliver foreign DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a novel, effective parallelization of the stochastic simulation algorithm (SSA) for pharmacokinetic models that characterize the rate-limiting, multi-step processes of intracellular gene delivery. While efficient parallelizations of the SSA are still an open problem in a general setting, the proposed parallel simulation method is able to substantially accelerate the next reaction selection scheme and the reaction update scheme in the SSA by exploiting and decomposing the structures of stochastic gene delivery models. This, thus, makes computationally intensive analysis such as parameter optimizations and gene dosage control for specific cell types, gene vectors, and transgene expression stability substantially more practical than that could otherwise be with the standard SSA. Here, we translated the nonviral gene delivery model based on mass-action kinetics by Varga et al. [Molecular Therapy, 4(5), 2001] into a more realistic model that captures intracellular fluctuations based on stochastic chemical kinetics, and as a case study we applied our parallel simulation to this stochastic model. Our results show that our simulation method is able to increase the efficiency of statistical analysis by at least 50% in various settings. © 2011 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.titleAn efficient parallel stochastic simulation method for analysis of nonviral gene delivery systemsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalProceedings of the 9th International Conference on Computational Methods in Systems Biology - CMSB '11en
dc.conference.date21 September 2011 through 23 September 2011en
dc.conference.name9th International Conference on Computational Methods in Systems Biology, CMSB'11en
dc.conference.locationParisen
dc.contributor.institutionLane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United Statesen
kaust.authorGao, Xinen
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