Fast and precise inference on diffusivity in interacting particle systems
Artikel i vetenskaplig tidskrift, 2023

Particle systems made up of interacting agents is a popular model used in a vast array of applications, not the least in biology where the agents can represent everything from single cells to animals in a herd. Usually, the particles are assumed to undergo some type of random movements, and a popular way to model this is by using Brownian motion. The magnitude of random motion is often quantified using mean squared displacement, which provides a simple estimate of the diffusion coefficient. However, this method often fails when data is sparse or interactions between agents frequent. In order to address this, we derive a conjugate relationship in the diffusion term for large interacting particle systems undergoing isotropic diffusion, giving us an efficient inference method. The method accurately accounts for emerging effects such as anomalous diffusion stemming from mechanical interactions. We apply our method to an agent-based model with a large number of interacting particles, and the results are contrasted with a naive mean square displacement-based approach. We find a significant improvement in performance when using the higher-order method over the naive approach. This method can be applied to any system where agents undergo Brownian motion and will lead to improved estimates of diffusion coefficients compared to existing methods.

Stochastic processes

Bayesian inference

Stochastic differential equations

Glioblastoma

Diffusion

Interacting particle systems

Agent based modelling

Författare

Gustav Lindwall

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Philip Gerlee

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Journal of Mathematical Biology

0303-6812 (ISSN) 1432-1416 (eISSN)

Vol. 86 5 64

Focus on glioblastoma: using patient-derived cell lines to decipher tumour expansion and evaluate new treatments

Stiftelsen för Strategisk forskning (SSF) (SB16-0066), 2019-01-01 -- 2021-12-31.

Ämneskategorier

Biofysik

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

DOI

10.1007/s00285-023-01902-y

PubMed

36991271

Mer information

Senast uppdaterat

2023-06-02