Reducing complexity in an agent based reaction model—Benefits and limitations of simplifications in relation to run time and system level output

Autor: David M. Rhodes, Mike Holcombe, Eva E. Qwarnstrom
Rok vydání: 2016
Předmět:
0301 basic medicine
Statistics and Probability
Mathematical optimization
Relation (database)
Scale (ratio)
Computer science
Models
Biological
Article
General Biochemistry
Genetics and Molecular Biology
03 medical and health sciences
0302 clinical medicine
Agent-based computational model
Modelling and Simulation
System level
Humans
Computer Simulation
Organic Chemicals
Duration (project management)
Simulation
Model reduction
Biochemistry
Genetics and Molecular Biology(all)
Applied Mathematics
NF-kappa B
Computational Biology
General Medicine
Complexity
Model complexity
Variety (cybernetics)
Scale
Kinetics
Increasing risk
030104 developmental biology
Models
Chemical
Runtime
Inorganic Chemicals
Modeling and Simulation
Reaction model
Limitations
Iterations
030217 neurology & neurosurgery
Signal Transduction
Zdroj: Bio Systems
ISSN: 0303-2647
DOI: 10.1016/j.biosystems.2016.06.002
Popis: Agent based modelling is a methodology for simulating a variety of systems across a broad spectrum of fields. However, due to the complexity of the systems it is often impossible or impractical to model them at a one to one scale. In this paper we use a simple reaction rate model implemented using the FLAME framework to test the impact of common methods for reducing model complexity such as reducing scale, increasing iteration duration and reducing message overheads. We demonstrate that such approaches can have significant impact on simulation runtime albeit with increasing risk of aberrant system behaviour and errors, as the complexity of the model is reduced.
Databáze: OpenAIRE
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