|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Process Algebra Models of Population Dynamics|
|Citation:||McCaig C, Norman R & Shankland C (2008) Process Algebra Models of Population Dynamics. In: Horimoto K, Regensburger G, Rosenkranz M & Yoshida H (eds.) Algebraic Biology. Lecture Notes in Computer Science, 5147. Algebraic Biology 2008, Castle of Hagenberg, Austria, 31.07.2008-02.08.2008. Berlin Heidelberg: Springer, pp. 139-155. http://www.springerlink.com/content/y26143833jl82307/?MUD=MP; https://doi.org/10.1007/978-3-540-85101-1|
|Series/Report no.:||Lecture Notes in Computer Science, 5147|
|Conference Name:||Algebraic Biology 2008|
|Conference Dates:||2008-07-31 - 2008-08-02|
|Conference Location:||Castle of Hagenberg, Austria|
|Abstract:||It is well understood that populations cannot grow without bound and that it is competition between individuals for resources which restricts growth. Despite centuries of interest, the question of how best to model density dependent population growth still has no definitive answer. We address this question here through a number of individual based models of populations expressed using the process algebra WSCCS. The advantage of these models is that they can be explicitly based on observations of individual interactions. From our probabilistic models we derive equations expressing overall population dynamics, using a formal and rigorous rewriting based method. These equations are easily compared with the traditionally used deterministic Ordinary Differential Equation models and allow evaluation of those ODE models, challenging their assumptions about system dynamics. Further, the approach is applied to epidemiology, combining population growth with disease spread.|
|Status:||AM - Accepted Manuscript|
|Rights:||Published by Springer Verlag. The original publication is available at www.springerlink.com|
|MNS08.pdf||Fulltext - Accepted Version||220.59 kB||Adobe PDF||View/Open|
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