Identification of compartmental models for perturbed cell populations using state-space parameter estimation techniques.
Multiple compartment models for describing synchronous cell kinetics in which cell populations are characterized by DNA content are reviewed. These models are useful for understanding and predicting a cell population's dynamic response to perturbations induced by drugs or radiation. A practical approach is proposed for determining the parameters of these models from empirical cell-cycle data. Specifically, a state-space parameter estimation algorithm based on the maximum likelihood method--developed and coded primarily for engineering applications and commercially available for personal computers and minicomputers--can be applied to DNA specific cell-cycle measurements from synchronized cell populations to produce model parameter estimates. This is demonstrated using published data from a cell-cycle experiment. The results show that the procedure works well, and that with careful experimental planning even better results should be possible. Since the compartmental model is often used to represent biological systems, this approach is widely applicable.
Department of Electrical Engineering, University of Washington, Seattle 98195.
G A
GA
Hayter
F J
FJ
Alexandro
D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology*; D003198:Computer Simulation; D004247:DNA / Q000032:analysis; D016013:Likelihood Functions; D008838:Microcomputers; D008905:Minicomputers; D008938:Mitosis / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical
D004247:DNA