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Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach.
Tomas Perez-Acle1, Ignacio Fuenzalida2, Alberto J M Martin1
1Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile; Centro Interdisciplinario de Neurociencia de Valparaiso, Universidad de Valparaíso, Valparaíso, Chile.
This study introduces PISKaS, a computational framework for multiscale stochastic simulations. It overcomes limitations of traditional differential equation models for complex biological systems, offering a more realistic approach.
Area of Science:
- Computational Biology
- Systems Biology
- Computational Modeling
Background:
- Traditional differential equation models for complex systems often make unrealistic assumptions in biological contexts.
- These models can entangle kinetics and causality, hindering scalability and individual behavior analysis.
Purpose of the Study:
- To present PISKaS, a rule-based framework for multiscale stochastic simulations.
- To demonstrate the application of PISKaS in modeling diverse complex systems across various scales.
Main Methods:
- Utilized the Stochastic Simulation Algorithm (SSA) for simulations.
- Reviewed three distinct models implemented in PISKaS: gene regulation, disease outbreak, and Game Theory.
Main Results:
- PISKaS facilitates continuous model improvement for gene regulatory networks (e.g., Escherichia coli).
- Modeled a hypothetical Ebola virus outbreak in a spatially compartmentalized environment.
- Developed a stochastic model for the Prisoner's Dilemma to analyze social interactions.
Conclusions:
- PISKaS provides a versatile platform for multiscale stochastic simulations of complex systems.
- The framework supports exploration of dynamics in biological and social systems.
- Demonstrated PISKaS's capability in handling diverse modeling challenges from molecular to social scales.
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