Stochastic Models in Mathematical Biology, 2nd Edition

Dear Colleagues,

Is biology deterministic or stochastic? Independent from the answer to this question, stochastic models have been applied with great success in all areas of mathematical biology. Applications extend beyond examples such as evolutionary processes or dynamics of biomolecules where the underlying dynamics themselves are considered stochastic.

For example, in population dynamics, stochastic models are used for describing population-level effects of processes occurring at the level of individuals. Examples include the description of stochastic extinction via branching processes and models for dispersal based on diffusion models.

Many biological systems, including ecosystems or neural networks in the brain, are influenced by a variety of processes—each of which, by itself, only has a small impact. The collective impact of these perturbations can be modelled by stochastic differential equations (SDEs), which enable us to study the dynamics of a deterministic process under stochastic fluctuations.

Finally, stochastic models enable us to represent parameter uncertainties in data-driven models obtained using a Bayesian statistics approach or to account for incomplete knowledge, for example, when considering a heterogeneous population of patients.

For this Special Issue, we are particularly interested in new approaches for applying stochastic models in biology as well as original applications of existing frameworks. We also invite manuscripts from the emerging field of parameterising stochastic models with experimental data.

Dr. Ivo Siekmann
Guest Editor

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• population dynamics
• mathematical physiology
• biophysics
• signalling
• Markov models
• Markov decision processes
• stochastic differential equations
• branching processes
• parametrisation of stochastic models
• Bayesian analysis

• Stochastic Models in Mathematical Biology in Mathematics (6 articles)