Particle-Based Stochastic Simulation in Systems Biology
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.
No Supplementary Data
No Article Media
Document Type: Research Article
Affiliations: EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton, CB10 1SD UK.
Publication date: 01 August 2006
More about this publication?
- Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.
The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.