Advanced Topics in Biological Systems Modeling

(Department)  Biomedical Engineering         (Division)      Bioelectric
 (Level and Major)  MSC and PhD

Course Title                   Advanced Topics in Biological Systems Modeling
Number of Credits       3             Prerequisite Digital Signal Processing
Lecturer Farzad Towhidkhah

Course Description
Biological systems are very complicated and hard to understand. 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 in understanding biological systems is modeling. In this course, different advanced 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 students the capability of describing biological systems from a systematic viewpoint and to learn different advanced methods of system identification and modeling.
Course Topics        
  • Introduction: Characteristics of biological systems (nonlinear, multi-input, multi-output, time-varying and etc.)
  • State space modelling
    • Recursive methods
    • Kalman filter
    • Subspace methods
  • Modeling discrete event systems
    • Hybrid systems
    • Discrete event systems
    • Queuing System
    • Petri nets
  • Cellular automata
  • Modeling using artificial neural networks
    • Feed Forward Neural Networks
    • Recurrent neural networks
  • Modeling using fuzzy logic
    • Fuzzy models
    • Fuzzy modeling of cell growth
  • Modeling of stochastic systems
    • "Markov chain" modeling
  • Modeling using wavelet
  • Modeling using chaos theory

The course aims to:
  • Understand the main characteristics of biological systems from computational viewpoints
  • Familiarize students with advanced methods and approaches in biological systems modeling
Reading Resources
  1. Ljung, L., & Glad, T. (1994). Modeling of dynamic systems. PTR Prentice Hall.
  2. Brown, M., & Harris, C. J. (1994). Neurofuzzy adaptive modelling and control. Prentice Hall.
  3. Dokholyan, N. V. (Ed.). (2012). Computational modeling of biological systems: from molecules to pathways. Springer Science & Business Media.
  4. Haefner, J. W. (2005). Modeling Biological Systems:: Principles and Applications. Springer Science & Business Media.
  5. Ruth, M., & Hannon, B. (1997). Modeling dynamic biological systems. In Modeling dynamic biological systems (pp. 3-27). Springer, New York, NY.
  6. Vafai, K. (Ed.). (2010). Porous media: applications in biological systems and biotechnology. CRC Press.
  7. Bajaj, A., & Wrycza, S. (2009). Systems analysis and design for advanced modeling methods: best practices.
  8. Forssell, U., & Ljung, L. (1999). Closed-loop identification revisited. Automatica35(7), 1215-1241.
  9. Ljung, L. (1999). Model validation and model error modeling. Linköping University Electronic Press.
Midterm Exam, Final Exam, Seminar, Final Project, Exercises

View: 267 Time(s)   |   Print: 55 Time(s)   |   Email: 0 Time(s)   |   0 Comment(s)