ArticleiScience2024
Unraveling biochemical spatial patterns: Machine learning approaches to the inverse problem of stationary Turing patterns.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Computational analysis of the Lengyel-Epstein system for the chlorite-iodide-malonic acid reaction using radial basis function neural network.Theory in biosciences = Theorie in den Biowissenschaften · 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
- Optimal network sizes for most robust Turing patterns.Scientific reports · 2025Article
- Learning spatio-temporal patterns with Neural Cellular Automata.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diffusion-driven Turing instability is a potential mechanism for spatial pattern formation in numerous biological and chemical systems. However, engineering these patterns and demonstrating that they are produced by this mechanism is challenging. To address this, we aim to solve the inverse problem in artificial and experimental Turing patterns. This task is challenging since patterns are often corrupted by noise and slight changes in initial conditions can lead to different patterns. We used both least squares to explore the problem and physics-informed neural networks to build a noise-robust method. We elucidate the functionality of our network in scenarios mimicking biological noise levels and showcase its application using an experimentally obtained chemical pattern. The findings reveal the significant promise of machine learning in steering the creation of synthetic patterns in bioengineering, thereby advancing our grasp of morphological intricacies within biological systems while acknowledging existing limitations.
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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 Socratic graph.