Abstract
Computational modeling and simulation have become invaluable tools for the biological sciences. Both aid in the formulation of new hypothesis and supplement traditional experimental research. Many different types of models using various mathematical formalisms can be created to represent any given biological system. Here we review a class of modeling techniques based on particle-based stochastic approaches. In these models, every reacting molecule is represented individually. Reactions between molecules occur in a probabilistic manner. Modeling problems caused by spatial heterogeneity and combinatorial complexity, features common to biochemical and cellular systems, are best addressed using Monte-Carlo single-particle methods. Several software tools implementing single-particle based modeling techniques are introduced and their various advantages and pitfalls discussed.
Keywords: Spatial modeling, discrete modeling, mesoscopic
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