A NUMERICAL FRAMEWORK FOR COMPUTING STEADY STATES OF STRUCTURED POPULATION MODELS AND THEIR STABILITY

作者：Mirzaev Inom; Bortz David M^*

来源：Mathematical Biosciences and Engineering, 2017, 14(4): 933-952.

DOI：10.3934/mbe.2017049

• Stationary solutions
• numerical stability analysis
• nonlinear evolution equations
• population balance equations
• size-structured population model
• Trotter-Kato theorem

Structured population models are a class of general evolution equations which are widely used in the study of biological systems. Many theoretical methods are available for establishing existence and stability of steady states of general evolution equations. However, except for very special cases, finding an analytical form of stationary solutions for evolution equations is a challenging task. In the present paper, we develop a numerical framework for computing approximations to stationary solutions of general evolution equations, which can also be used to produce approximate existence and stability regions for steady states. In particular, we use the Trotter-Kato Theorem to approximate the infinitesimal generator of an evolution equation on a finite dimensional space, which in turn reduces the evolution equation into a system of ordinary differential equations. Consequently, we approximate and study the asymptotic behavior of stationary solutions. We illustrate the convergence of our numerical framework by applying it to a linear Sinko-Streifer structured population model for which the exact form of the steady state is known. To further illustrate the utility of our approach, we apply our framework to nonlinear population balance equation, which is an extension of well-known Smoluchowski coagulation-fragmentation model to biological populations. We also demonstrate that our numerical framework can be used to gain insight about the theoretical stability of the stationary solutions of the evolution equations. Furthermore, the open source Python program that we have developed for our numerical simulations is freely available from our GitHub repository (github.com/MathBioCU).

• 出版日期2017-8