A Bayesian Approach to Modelling Biological Pattern Formation with Limited Data

TL;DR

This paper introduces a Bayesian statistical method to identify parameters in biological pattern formation models from limited data, addressing challenges posed by unstable dynamics and stationary observations.

Contribution

It extends the Correlation Integral Likelihood method with modifications for improved accuracy and provides GPU-accelerated implementations for various pattern formation models.

Findings

Enhanced parameter identification accuracy with limited data

Effective comparison of pattern statistics rather than raw data

GPU-based solvers enable efficient computations

Abstract

Pattern formation in biological tissues plays an important role in the development of living organisms. Since the classical work of Alan Turing, a pre-eminent way of modelling has been through reaction-diffusion mechanisms. More recently, alternative models have been proposed, that link dynamics of diffusing molecular signals with tissue mechanics. In order to distinguish among different models, they should be compared to experimental observations. However, in many experimental situations only the limiting, stationary regime of the pattern formation process is observable, without knowledge of the transient behaviour or the initial state. The unstable nature of the underlying dynamics in all alternative models seriously complicates model and parameter identification, since small changes in the initial condition lead to distinct stationary patterns. To overcome this problem the initial state of the model can be randomised. In the latter case, fixed values of the model parameters correspond to a family of patterns rather than a fixed stationary solution, and standard approaches to compare pattern data directly with model outputs, e.g., in the least squares sense, are not suitable. Instead, statistical characteristics of the patterns should be compared, which is difficult given the typically limited amount of available data in practical applications. To deal with this problem, we extend a recently developed statistical approach for parameter identification using pattern data, the so-called Correlation Integral Likelihood (CIL) method. We suggest modifications that allow increasing the accuracy of the identification process without resizing the data set. The proposed approach is tested using different classes of pattern formation models. For all considered equations, parallel GPU-based implementations of the numerical solvers with efficient time stepping schemes are provided.