Discrete Event Multi-level Models for Systems Biology

Abstract

Abstract. Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the "macro" level, at the "micro" level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

A computational framework for modelling multicellular biochemistry

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“…To reduce a model's complexity, the level of detail at which subsystems are described might be chosen differently, leading to multi-level modeling (Uhrmacher, Degenring, and Zeigler 2005). If part of the model is described as a deterministic, continuous model and other parts as stochastic, discrete event models, reliable hybrid methods for combining continuous with stochastic models become necessary.…”

A component-based simulation layer for JAMES

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