Dynamical Modeling of Biological Systems
Par šo e-grāmatu
The book covers deterministic models (for example differential equations), stochastic models (for example Markov chains and autoregressive models) and model-independent aspects of time series analysis. Plenty of examples and exercises are included, often taken or inspired from the scientific literature, and covering a broad range of topics such as neuroscience, cell biology, genetics, evolution, ecology, microbiology, physiology, epidemiology and conservation. The book delivers generic modeling techniques used across a wide range of situations in biology, and hence readers from other scientific disciplines will find that much of the material is also applicable in their own field. Proofs of most mathematical statements are included for the interested reader, but are not essential for a practical understanding of the material.
The book introduces the popular scientific programming language MATLAB as a tool for simulating models, fitting models to data, and visualizing data and model predictions. The material taught is current as of MATLAB version 2022b. The material is taught in a sufficiently general way that also permits the use of alternative programming languages.
Par autoru
Stilianos Louca is an Assistant Professor at the University of Oregon, Department of Biology. He holds a German Diplom in physics, a BSc in mathematics and a PhD in applied mathematics. He is an expert in mathematical modeling of biological systems, biostatistics and bioinformatics. His lab's research focuses on the ecology and evolution of microorganisms, mainly using computational approaches but also including experiments and field surveys. He has authored over 40 peer-reviewed publications in international scientific journals.
