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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Popis
Shrnutí: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.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbp020