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Stochastic approaches in systems biology.
Mukhtar Ullah1, Olaf Wolkenhauer1
1Systems Biology and Bioinformatics Group, University of Rostock, 18051 Rostock, Germany.
Wiley Interdisciplinary Reviews. Systems Biology and Medicine
|September 14, 2010
Summary
Systems biology requires stochastic methods due to random chemical reactions and low molecule counts. This review explains key stochastic approaches like the chemical master equation (CME) and simulation algorithms for modeling these systems.
Area of Science:
- Biochemistry
- Computational Biology
- Systems Biology
Background:
- Biological systems exhibit inherent randomness at the molecular level.
- Low molecule numbers and reactions far from equilibrium necessitate stochastic modeling.
Purpose of the Study:
- To provide an overview of essential stochastic approaches for systems biology.
- To introduce probability concepts alongside biochemical reaction systems for intuitive understanding.
Main Methods:
- Explanation of propensity, chemical master equation (CME), and stochastic simulation algorithm derived from the Markov property.
- Discussion of analytical approximations including the chemical Langevin equation, Fokker-Planck equation, and two-moment approximation (2MA).
Main Results:
- A framework for modeling subcellular biochemical systems using stochastic methods is presented.
- Comparison of stochastic simulation with analytical approximations for gaining insights into randomness.
Conclusions:
- The review clarifies key concepts in stochastic modeling for biochemical networks.
- Readers are prepared for advanced texts on stochastic systems biology.
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