Abstract
Complex systems are governed by dynamic processes whose underlying causal rules are difficult to unravel. However, chemical reactions, molecular interactions, and many other complex systems can be usually represented as concentrations or quantities that vary over time, which provides a framework to study these dynamic relationships. An increasing number of tools use these quantifications to simulate dynamically complex systems to better understand their underlying processes. The application of such methods covers several research areas from biology and chemistry to ecology and even social sciences.
In the following chapter, we introduce the concept of rule-based simulations based on the Stochastic Simulation Algorithm (SSA) as well as other mathematical methods such as Ordinary Differential Equations (ODE) models to describe agent-based systems. Besides, we describe the mathematical framework behind Kappa (κ), a rule-based language for the modeling of complex systems, and some extensions for spaßtial models implemented in PISKaS (Parallel Implementation of a Spatial Kappa Simulator). To facilitate the understanding of these methods, we include examples of how these models can be used to describe population dynamics in a simple predator–prey ecosystem or to simulate circadian rhythm changes.
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Acknowledgements
The authors would like to kindly acknowledge the financial support received from FONDECYT Inicio 11140342 and award numbers FA9550-16-1-0111 and FA9550-16-1-0384 of the USA Air Force Office of Scientific Research. This research was partially supported by the supercomputing infrastructure of the Chilean NLHPC [ECM-02]. Basal Funding Program from CONICYT PFB-16 to Fundacion Ciencia & Vida and Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaiso CINV ICM-Economia [P09-022-F].
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Bustos, Á., Fuenzalida, I., Santibáñez, R., Pérez-Acle, T., Martin, A.J.M. (2018). Rule-Based Models and Applications in Biology. In: von Stechow, L., Santos Delgado, A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8618-7_1
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DOI: https://doi.org/10.1007/978-1-4939-8618-7_1
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