ArticleNature communications2016
Cox process representation and inference for stochastic reaction-diffusion processes.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- R-package agentBayes: Likelihood-based statistical methods for agent-based models.PLoS computational biology · 2026Article
- Actin network heterogeneity tunes activator-inhibitor dynamics at the cell cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation.Computational and structural biotechnology journal · 2025Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Article
- Modeling membrane reshaping driven by dynamic protein assemblies.Current opinion in structural biology · 2023Review
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Neural field models for latent state inference: Application to large-scale neuronal recordings.PLoS computational biology · 2019Article
- Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.Journal of the Royal Society, Interface · 2019Article
- Inferring gene regulatory networks from single-cell data: a mechanistic approach.BMC systems biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.