Numerical Methods for Stochastic Modeling of Genes and Proteins
Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolutio...
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| Format: | Doctoral Thesis |
| Language: | English |
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Uppsala universitet, Avdelningen för teknisk databehandling
2007
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8293 http://nbn-resolving.de/urn:isbn:978-91-554-7009-8 |
| Summary: | Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolution of the the probability density function of the often high-dimensional state space. The CME is approximated by a partial differential equation (PDE), the Fokker-Planck equation and solved numerically. Direct solution of the CME rapidly becomes computationally expensive for increasingly complex biological models, since the state space grows exponentially with the number of dimensions. Adaptive numerical methods can be applied in time and space in the PDE framework, and error estimates of the approximate solutions are derived. A method for splitting the CME operator in order to apply the PDE approximation in a subspace of the state space is also developed. The performance is compared to the most widely spread alternative computational method. |
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