Abstract
This chapter looks at modeling of biochemical systems when the assumption of perfect mixing is relaxed and spatial configurations of molecules need to be taken into account. Spatial simulations not only introduce additional degrees of freedom in the system, but demand a somewhat different way of thinking about the model. This chapter introduces the reader conceptually to spatial modeling but also contains two walk-through examples. It uses the widely respected Smoldyn simulation software to illustrate the modeling process in spatial systems. The case study in this model is a biochemical change detector.
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Chu, D., von der Haar, T.: The architecture of eukaryotic translation. Nucleic Acids Res. 40(20) (2012). doi:10.1093/nar/gks825
Andrews, S.S.: Spatial and stochastic cellular modeling with the Smoldyn simulator. In: Bacterial Molecular Networks, pp. 519–542. Springer, Berlin (2012)
Andrews, S.S.: A spatial stochastic simulator for chemical reaction networks. http://www.smoldyn.org/. Accessed 19 June 2015
Yi, T., Huang, Y., Simon, M., Doyle, J.: Robust perfect adaptation in bacterial chemotaxis through integral feedback control. Proc. Natl. Acad. Sci. USA 97, 4649–4653 (2000)
De Palo, G., Endres, R.G.: Unraveling adaptation in eukaryotic pathways: lessons from protocells. PLoS Comput. Biol. 9(10), e1003,300 (2013)
Hepburn, I., Chen, W., Wils, S., De Schutter, E.: Steps: efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Syst. Biol. 6(1), 36 (2012). doi:10.1186/1752-0509-6-36
STochastic Engine for Pathway Simulation. http://steps.sourceforge.net/STEPS/default.php. Accessed 19 June 2015
Kerr, R., Bartol, T., Kaminsky, B., Dittrich, M., Chang, J., Baden, S., Sejnowski, T., Stiles, J.: Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J. Sci. Comput. 30(6), 3126–3149 (2008)
MCell: Monte Carlo cell. http://mcell.org. Accessed 19 June 2015
Foundation, B.: Blender. https://www.blender.org/. Accessed 19 June 2015
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Barnes, D.J., Chu, D. (2015). Biochemical Models Beyond the Perfect Mixing Assumption. In: Guide to Simulation and Modeling for Biosciences. Simulation Foundations, Methods and Applications. Springer, London. https://doi.org/10.1007/978-1-4471-6762-4_8
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DOI: https://doi.org/10.1007/978-1-4471-6762-4_8
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