Dynamical Modeling Methods for Systems Biology
Learn dynamical modeling techniques used in Systems Biology research. This course covers ordinary differential equation-based models, partial differential equation-based models, and stochastic models. Suitable for advanced undergraduates and beginning graduate students. Gain insights into biological processes and develop computational modeling skills. Ideal for those planning to conduct original research or use experimental techniques in the laboratory. Enhance your understanding and generate testable predictions with simulations using MATLAB.
An introduction to dynamical modeling techniques used in contemporary Systems Biology research.
We take a case-based approach to teach contemporary mathematical modeling
techniques. The course is appropriate for advanced undergraduates and beginning graduate students. Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes. The course will be useful for students who plan to use experimental techniques as their approach in the laboratory and employ computational modeling as a tool to draw deeper understanding of experiments. The course should also be valuable as an introductory overview for students planning to conduct original research in modeling biological systems.
This course focuses on dynamical modeling techniques used in Systems Biology research. These techniques are based on biological mechanisms, and simulations with these models generate predictions that can subsequently be tested experimentally. These testable predictions frequently provide novel insight into biological processes. The approaches taught here can be grouped into the following categories: 1) ordinary differential equation-based models, 2) partial differential equation-based models, and 3) stochastic models.
What you will learn
Introduction | Computing with MATLAB
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Introduction to Dynamical Systems
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Bistability in Biochemical Signaling Models
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Computational Modeling of the Cell Cycle
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