Department of Biomedical Engineering

Course Description
Biological systems are so complicated and too hard to understanding. Numerous ethical issues are usually requested to perform any experiment on biological systems. These experiments involve a lot of energy and cost. One of the tools to understanding biological systems is modeling. In this course, different classical and basic methods and approaches are introduced to identify and model biological systems computationally.

Course Goals and Objectives
The main goal of this course is to give the student the capability of describing biological system from a systematic viewpoint and to learn different classical and basic methods of system identification and modeling.

Course Topics

Chapter 1 - Introduction
- Basic definitions and terms
- Types of models
- Modelling approaches (Analytical and Experimental)
Chapter 2 - Analytical Modeling
- Analytical modeling steps
- Analog systems (Electrical, Mechanical, Chemical)
Chapter 3 - A Review of the Possibilities and Statistics
- Random variables
- Random processes
- Statistical models and testing of hypotheses
Chapter 4 - Experimental Modeling (System Identification)
- Classical temporal and frequency methods (impulse response, step response, frequency response)
- Correlation Analysis
- Frequency spectrum estimation
- Parametric methods
Chapter 5 - Parameter Estimation Methods
- Least Square method
- Instrumental Variable method
- Maximum likelihood method
- Prediction error method
Chapter 6 – State space modeling
Chapter 7 - Transfer of substances in the body and its models
- Material transfer by fluid flow
- Material transfer by infusion
- Compartmental Models
Chapter 8 - Practical Examples of Modeling Biological and Physiological Systems
Circulation system modeling
Respiratory system modeling
Modeling of body movement control system
modeling of body heat transfer system
Chapter 9 - New topics in modeling nonlinear and biological systems

The course aims to:

Understanding the main characteristics of biological systems from computational viewpoints
Being familiar with system identification basic methods and modelling approaches

Reading Resources

Haefner, J. W. (2005). Modeling Biological Systems:: Principles and Applications. Springer Science & Business Media.
Dokholyan, N. V. (Ed.). (2012). Computational modeling of biological systems: from molecules to pathways. Springer Science & Business Media.
Rao, V. S. H., & Rao, P. R. S. (2009). Dynamic models and control of biological systems. Springer Science & Business Media.
Rideout, V. C. (1991). Mathematical and computer modeling of physiological systems (p. 71). Englewood Cliffs, NJ:: Prentice Hall.
Spain, J. D. (1982). BASIC microcomputer models in biology. Adison.
Adams, L. (1967). Applications of Control Theory to Physiological Systems Howard T. Milhorns.
Ljung, L. (1999). System identification. Wiley encyclopedia of electrical and electronics engineering, 1-19.
Ljung, L., & Glad, T. (1994). Modeling of dynamic systems. PTR Prentice Hall.
Norton, J. P. (2009). An introduction to identification. Courier Corporation.
Ljung, L., & Söderström, T. (1983). Theory and practice of recursive identification. MIT press.
Söderström, T., & Stoica, P. (1989). System identification. Prentice-Hall International.

Evaluation

Midterm Exam, Final Exam, Seminar, Final Project, Exercises

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