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Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics.
BMC Syst Biol. 2017 03 14; 11(Suppl 3):21.BS

Abstract

BACKGROUND

Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity.

RESULTS

In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method.

CONCLUSION

Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system.

Authors+Show Affiliations

Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA. ycao@cs.vt.edu.

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

28361679

Citation

Chen, Minghan, et al. "Stochastic Modeling and Simulation of Reaction-diffusion System With Hill Function Dynamics." BMC Systems Biology, vol. 11, no. Suppl 3, 2017, p. 21.
Chen M, Li F, Wang S, et al. Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics. BMC Syst Biol. 2017;11(Suppl 3):21.
Chen, M., Li, F., Wang, S., & Cao, Y. (2017). Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics. BMC Systems Biology, 11(Suppl 3), 21. https://doi.org/10.1186/s12918-017-0401-9
Chen M, et al. Stochastic Modeling and Simulation of Reaction-diffusion System With Hill Function Dynamics. BMC Syst Biol. 2017 03 14;11(Suppl 3):21. PubMed PMID: 28361679.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Stochastic modeling and simulation of reaction-diffusion system with Hill function dynamics. AU - Chen,Minghan, AU - Li,Fei, AU - Wang,Shuo, AU - Cao,Young, Y1 - 2017/03/14/ PY - 2017/4/1/entrez PY - 2017/4/1/pubmed PY - 2017/10/17/medline KW - Hill function KW - Hybrid method KW - Reaction diffusion master equation (RDME) KW - Stochastic simulation SP - 21 EP - 21 JF - BMC systems biology JO - BMC Syst Biol VL - 11 IS - Suppl 3 N2 - BACKGROUND: Stochastic simulation of reaction-diffusion systems presents great challenges for spatiotemporal biological modeling and simulation. One widely used framework for stochastic simulation of reaction-diffusion systems is reaction diffusion master equation (RDME). Previous studies have discovered that for the RDME, when discretization size approaches zero, reaction time for bimolecular reactions in high dimensional domains tends to infinity. RESULTS: In this paper, we demonstrate that in the 1D domain, highly nonlinear reaction dynamics given by Hill function may also have dramatic change when discretization size is smaller than a critical value. Moreover, we discuss methods to avoid this problem: smoothing over space, fixed length smoothing over space and a hybrid method. CONCLUSION: Our analysis reveals that the switch-like Hill dynamics reduces to a linear function of discretization size when the discretization size is small enough. The three proposed methods could correctly (under certain precision) simulate Hill function dynamics in the microscopic RDME system. SN - 1752-0509 UR - https://www.unboundmedicine.com/medline/citation/28361679/Stochastic_modeling_and_simulation_of_reaction_diffusion_system_with_Hill_function_dynamics_ L2 - https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-017-0401-9 DB - PRIME DP - Unbound Medicine ER -