Mathematical Modeling and Data Science for Biology and Medicine, 2nd Edition

Dear Colleagues,

The importance of mathematical modeling and data science is growing for understanding biological events and medical applications. This Special Issue, as a follow up to the previous edition, will attempt to study these still-mysterious matters observed in living things from mathematics and data science, covering topics including mathematical modelling of complex biological systems, optimal control strategies for biological systems, numerical methods for solving differential equations in biology and healthcare models, stochastic and hybrid models in biology and medicine, data-driven modeling of biology and medicine, statistics and data science for biological and biomedical data analysis, etc.

Here is a list of examples of biological questions for which a new mathematical approach is expected. New ideas, concepts, models, analysis, predictions, biological, and medical applications are welcome:

1. Dynamic mechanism of complex biological systems
2. Biological reactions caused by several interactions of stimulations, such as high and low temperatures, pH, osmotic pressures, cytokines, hormones, viruses, etc.;
3. Analysis of the effect of multisensing in multi-scale biological events;
4. Communications between heterogeneous cells, reactions to organs, and their interactions;
5. Biological homeostasis through a reaction network and its breakdown;
6. The role of the microenvironment in the malignancy of cancer cells;
7. Mathematical modeling of signal transmission and cross-talks of signals inside and outside cells;
8. Data science methods used to detect biological mechanisms which were not known before, and applications;
9. New diagnoses and therapies using mathematical methods;
10. New mathematical concepts motivated by biological and medical events, their analysis, and applications;
11. How to deal with social data of healthcare and the deceased to construct mathematical models to predict events.
12. Multi-scale modeling and agent-based simulation of cell–cell interactions.
13. Mathematical methods for the detection and prediction of gene expression and metabolism.

Prof. Dr. Takashi Suzuki
Guest Editor

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• mathematical oncology
• mathematical immunology
• mathematical epidemiology
• population dynamics
• mathematical methods in diagnosis and therapy
• systems biology
• infectious disease modeling
• epidemiological dynamics
• human behaviors evolution dynamics
• biomedical data analysis
• regression models
• data-driven modeling Machine learning approaches
• biological signal analysis
• data science
• multi-scale modeling