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Bifurcations and traveling waves in a delayed partial differential equation
Alejandro D. Rey1, Michael C. Mackey
1Department of Chemical Engineering, McGill University, 3480 University Street, Montreal, Quebec H3A 2A7, CanadaDepartments of Physiology, Physics, and Mathematics, and Centre for Nonlinear Dynamics in Physiology and Medicine, McGill University, 3655 Drummond Street, Montreal H3G 1Y6, Canada.
Chaos (Woodbury, N.Y.)
|April 1, 1992
Summary
This study models cell population dynamics with proliferation and maturation using a nonlinear partial differential equation. It identifies various biological solutions, including trivial, stationary, periodic, and traveling waves, based on initial conditions and parameters.
Area of Science:
- Mathematical Biology
- Nonlinear Dynamics
- Partial Differential Equations
Background:
- Cell population dynamics involve complex processes like proliferation and maturation.
- Mathematical models are crucial for understanding these dynamics.
- Previous models may not fully capture simultaneous proliferation and maturation with temporal and maturation delays.
Purpose of the Study:
- To develop and analyze a mathematical model for cell populations with simultaneous proliferation and maturation.
- To investigate the diverse solution behaviors arising from this model.
- To delineate parameter space regions for different solution types and their stability.
Main Methods:
- Formulation of a nonlinear first-order partial differential equation with time and maturation retardation.
- Analysis of model solutions based on initial functions and a three-component parameter vector.
- Investigation of homogeneous and inhomogeneous stationary, periodic, traveling wave, and chaotic wave solutions.
Main Results:
- Identified three biologically relevant homogeneous solutions for positive initial functions: trivial, stationary, and periodic.
- Discovered multiple solution types for zero initial conditions, including singular, traveling, slow traveling, and slow chaotic traveling waves.
- Mapped regions of parameter space corresponding to the existence and local stability of these solutions.
Conclusions:
- The model exhibits rich dynamics, producing a spectrum of solutions dependent on initial conditions and parameters.
- Understanding these solutions is key to predicting cell population behavior in various biological contexts.
- The study provides a framework for analyzing complex cell dynamics through mathematical modeling.