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William Holmes
Last active: 2/26/2016

An efficient, nonlinear stability analysis for detecting pattern formation in reaction diffusion systems.

Holmes WR
Bull Math Biol. 2014 76 (1): 157-83

PMID: 24158538 · PMCID: PMC4117191 · DOI:10.1007/s11538-013-9914-6

Reaction diffusion systems are often used to study pattern formation in biological systems. However, most methods for understanding their behavior are challenging and can rarely be applied to complex systems common in biological applications. I present a relatively simple and efficient, nonlinear stability technique that greatly aids such analysis when rates of diffusion are substantially different. This technique reduces a system of reaction diffusion equations to a system of ordinary differential equations tracking the evolution of a large amplitude, spatially localized perturbation of a homogeneous steady state. Stability properties of this system, determined using standard bifurcation techniques and software, describe both linear and nonlinear patterning regimes of the reaction diffusion system. I describe the class of systems this method can be applied to and demonstrate its application. Analysis of Schnakenberg and substrate inhibition models is performed to demonstrate the methods capabilities in simplified settings and show that even these simple models have nonlinear patterning regimes not previously detected. The real power of this technique, however, is its simplicity and applicability to larger complex systems where other nonlinear methods become intractable. This is demonstrated through analysis of a chemotaxis regulatory network comprised of interacting proteins and phospholipids. In each case, predictions of this method are verified against results of numerical simulation, linear stability, asymptotic, and/or full PDE bifurcation analyses.

MeSH Terms (12)

Chemotaxis Computer Simulation Diffusion GTP Phosphohydrolases Kinetics Linear Models Mathematical Concepts Models, Biological Nonlinear Dynamics Pattern Recognition, Automated Phosphatidylinositols Systems Biology

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