Taming the complexity of biological pathways through parallel computing

Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the...

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Vydané v:	Briefings in bioinformatics Ročník 10; číslo 3; s. 278 - 288
Hlavní autori:	Ballarini, Paolo, Guido, Rosita, Mazza, Tommaso, Prandi, Davide
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Oxford University Press 01.05.2009 Oxford Publishing Limited (England)
Predmet:	Algorithms Bioinformatics Biological and medical sciences Computational Biology - methods Computer Simulation Differential equations Fundamental and applied biological sciences. Psychology General aspects Mathematical models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Biological Parallel processing Software Stochastic models Stochastic Processes Systems Integration model checking ODE numerical solutions stochastic simulation biological pathways parallel computing Parallel processing Stochastic simulation Models Complexity
ISSN:	1467-5463, 1477-4054, 1477-4054
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Abstract	Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.
AbstractList	Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained. [PUBLICATION ABSTRACT] Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained. Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.
Author	Guido, Rosita Prandi, Davide Ballarini, Paolo Mazza, Tommaso
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SubjectTerms	Algorithms Bioinformatics Biological and medical sciences Computational Biology - methods Computer Simulation Differential equations Fundamental and applied biological sciences. Psychology General aspects Mathematical models Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Biological Parallel processing Software Stochastic models Stochastic Processes Systems Integration
Title	Taming the complexity of biological pathways through parallel computing
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