Tom Thorne - Parameter inference with topological approximate Bayesian Computation
Channel: BioInference
Date: 2022-06-28

Parameter inference with topological approximate Bayesian Computation

Tom Thorne
University of Surrey

BioInference 2022
https://bioinference.github.io/

00:00 Introduction
00:15 Talk
22:52 Q&A

Abstract:
Inferring the parameters of models describing biological systems is an important problem in the reverse engineering of the mechanisms underlying these systems. Much work has focused on parameter inference of stochastic and ordinary differential equation models using Approximate Bayesian Computation (ABC). While there is some recent work on inference in spatial models, this remains an open problem. Simultaneously, advances in topological data analysis (TDA), a field of computational mathematics, have enabled spatial patterns in data to be characterised. Building on recent work using topological data analysis to study different regimes of parameter space for the Anderson-Chaplain model of angiogenesis, we propose a novel method for combining TDA with ABC to infer parameters in the Anderson-Chaplain model. By applying tools from persistent homology we are able to capture the connectivity of the data and infer model parameters that reproduce the qualitative behaviour observed in the data. We demonstrate that this topological approach outperforms ABC approaches that use simpler statistics based on spatial features of the data. This represents a first step towards a general framework of spatial parameter inference for biological systems.