A Weakly Nonlinear Theory for Pattern Formation in Structured Models with Localized Solutions

TL;DR

This paper develops a weakly nonlinear analysis framework for structured PDE models with localized steady states, enabling the study of pattern formation near instabilities in complex biological systems.

Contribution

It introduces a novel weakly nonlinear approach using WKBJ asymptotics and Stokes analysis for models with localized steady states, extending classical methods to new settings.

Findings

Derives an amplitude equation governing pattern dynamics near bifurcation.

Predicts whether bifurcation is subcritical or supercritical based on parameter signs.

Demonstrates broad applicability of the method beyond the specific example.

Abstract

Structured models, such as PDEs structured by age or phenotype, provide a setting to study pattern formation in heterogeneous populations. Classical tools to quantify the emergence of patterns, such as linear and weakly nonlinear analyses, pose significant mathematical challenges for these models due to sharply peaked or singular steady states. Here, we present a weakly nonlinear framework that extends classical tools to structured PDE models in settings where the base state is spatially uniform, but exponentially localized in the structured variable. Our approach utilizes WKBJ asymptotics and an analysis of the Stokes phenomenon to systematically resolve the solution structure in the limit where the steady state tends to a Dirac-delta function. To demonstrate our method, we consider a chemically structured (nonlocal) model of motile bacteria that interact through quorum sensing. For this example, our analysis yields an amplitude equation that governs the solution dynamics near a linear instability, and predicts a pitchfork bifurcation. From the amplitude equation, we deduce an effective parameter grouping whose sign determines whether the pitchfork bifurcation is subcritical or supercritical. Although we demonstrate our framework for a specific example, our techniques are broadly applicable.