ECE 695R - Control of Cellular Processes
Course Details
Lecture Hours: 3 Credits: 3
Counts as:
Experimental Course Offered:
Spring 2008
Catalog Description:
Addresses the theoretical basis of adaptive and model predictive control theory techniques from the perspective of the regulation of cell behavior and function. Adaptive and model predictive controllers can regulate nonlinear, stochastic, hybrid, time-varying, and uncertain systems; cellular processes can exhibit all of these system properties. Controllers for mathematical models of cellular processes will be designed to manipulate cellular systems to obtain desired behaviors. Control topics will include self-tuning regulators, model reference adaptive systems, and receding horizon model predictive control Cellular processes will include batch cell growth, cell cycle, signal transduction, and gene expression.
Required Text(s):
- Adaptive Control , 2nd Edition , K.J. Astrom, B. Wittenmark , Dover , 1995 , ISBN No. 0486462781
Recommended Text(s):
- Molecular Biology of the Cell , 4 Edition , B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter , Garland , 2002
- Receding Horizon Control: Model predictive control for state models , W.H. Kwon and S. Han , Springer , 2005
Lecture Outline:
| Week(s) | Major Topics |
|---|---|
| 1 | Introduction to adaptive control approaches and their applications |
| 2,3 | Real time parameter estimation and challenges with interfacing with experimental cell biology |
| 4,5 | Model-reference adaptive systems with applications to cell cycle |
| 6,7 | Self-tuning regulators for cell differentiation |
| 8 | Stability, convergence and robustness of adaptive controllers |
| 9,10 | Stochastic adaptive control with applications in gene expression |
| 11 | Introduction to receding horizon model predictive control |
| 12,13 | State feedback in model predictive control with applications to bioreactor design |
| 14,15 | Output feedback in model predictive control with application to signal transduction |
Assessment Method:
none