## Modeling IP3 receptor function using stochastic approaches

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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**Modeling IP3 receptor function using stochastic approaches.** / Intosalmi, J.; Manninen, T.; Hituri, K.; Ruohonen, K.; Linne, M-L.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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*Proceedings of the Fifth International Workshop on Computational Systems Biology, WCSB 2008, Leipzig, Germany, 11-13 June 2008.*pp. 69-72.

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*Proceedings of the Fifth International Workshop on Computational Systems Biology, WCSB 2008, Leipzig, Germany, 11-13 June 2008*(pp. 69-72)

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TY - GEN

T1 - Modeling IP3 receptor function using stochastic approaches

AU - Intosalmi, J.

AU - Manninen, T.

AU - Hituri, K.

AU - Ruohonen, K.

AU - Linne, M-L.

N1 - Yhteisjulkaisu MAT:n kanssa<br/>Contribution: organisation=sgn,FACT1=0.5<br/>Contribution: organisation=mat,FACT2=0.5

PY - 2008

Y1 - 2008

N2 - The time evolution of chemical systems is traditionally modeled using deterministic ordinary differential equations. Chemical reactions, however, are random in nature, and the deterministic approach is valid only for a restricted class of systems. Stochastic models take random fluctuations into account and are thus more realistic. In this work, we simulate an inositol trisphosphate receptor model using ordinary differential equations, stochastic differential equations, and the Gillespie stochastic simulation algorithm. The main goal of this work is to study the applicability of these methods for a system containing small numbers of molecules and ions. We concentrate especially on the SDE approach and investigate how well it models systems with small numbers of chemical species.

AB - The time evolution of chemical systems is traditionally modeled using deterministic ordinary differential equations. Chemical reactions, however, are random in nature, and the deterministic approach is valid only for a restricted class of systems. Stochastic models take random fluctuations into account and are thus more realistic. In this work, we simulate an inositol trisphosphate receptor model using ordinary differential equations, stochastic differential equations, and the Gillespie stochastic simulation algorithm. The main goal of this work is to study the applicability of these methods for a system containing small numbers of molecules and ions. We concentrate especially on the SDE approach and investigate how well it models systems with small numbers of chemical species.

M3 - Conference contribution

SN - 978-952-15-1988-8

SP - 69

EP - 72

BT - Proceedings of the Fifth International Workshop on Computational Systems Biology, WCSB 2008, Leipzig, Germany, 11-13 June 2008

A2 - Ahdesmäki, M.

ER -