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2. Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Cornwall TR10 9FE, UK
Analytical and Numerical Methods for Stochastic Biological Systems
Topic Information
Dear Colleagues,
Stochastic mathematical models have been recognized as the most effective tools for understanding, analyzing, and predicting the real-world problems that humanity faces every day. These problems are addressed and introduced by researchers working in all fields of applied sciences. Perhaps the major challenge for researchers is the process of mathematical analysis and prediction. To analyze these models, one must solve them numerically or analytically and use the solution to make predictions based on the shape of perturbations and its associated theoretical framework. Due to the complexity of these models, new numerical charts and analysis methods have been developed to solve these models. Generally, this Topics aims to publish original and high-quality research articles covering the new analytical and numerical methods for perturbed real-world biological systems. In this context, continuous and discrete time stochastic processes will be discussed, as well as stochastic differential equations, stochastic partial differential equation, Lévy processes, first-passage-time problems, stochastic optimal control, parameter estimation, and advanced simulation techniques. All the above topics are intended to be treated in the spirit of modeling the evolution of stochastic systems of interest in biology. Potential topics include but are not limited to the following:
- Stability analysis for stochastic differential equations in biology.
- Partial differential equation models in biology.
- Jump-diffusion processes.
- Markov and semi-Markov processes.
- Stochastic optimal control.
- Numerical solutions of stochastic differential equations.
- Parameter Estimation in stochastic differential equations.
The aim of this Topics is to compile a collection of articles reflecting the latest developments in stochastic modeling in biology, with the aim of studying the qualitative and quantitative behavior of phenomena in which a random component is necessary.
Dr. Mehmet Yavuz
Prof. Dr. Necati Ozdemir
Prof. Dr. Mouhcine Tilioua
Prof. Dr. Yassine Sabbar
Topic Editors
Keywords
- stochastic differential equations in biology
- partial differential equation in biology
- jump-diffusion processes
- Markov and semi-Markov processes
Participating Journals
| Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC |
|---|---|---|---|---|---|
Algorithms
|
1.8 | 4.1 | 2008 | 15 Days | CHF 1600 |
Computation
|
1.9 | 3.5 | 2013 | 19.7 Days | CHF 1800 |
Entropy
|
2.1 | 4.9 | 1999 | 22.4 Days | CHF 2600 |
Fractal and Fractional
|
3.6 | 4.6 | 2017 | 20.9 Days | CHF 2700 |
Mathematical and Computational Applications
|
1.9 | - | 1996 | 28.8 Days | CHF 1400 |
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