Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909935149776896 |
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| author | Matas-Gil, Antonio Endres, Robert G. |
| author_facet | Matas-Gil, Antonio Endres, Robert G. |
| contents | 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 high levels of noise corrupt the patterns 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 through a prototype involving 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06339 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns Matas-Gil, Antonio Endres, Robert G. Biological Physics Dynamical Systems Pattern Formation and Solitons Quantitative Methods 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 high levels of noise corrupt the patterns 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 through a prototype involving 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. |
| title | Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns |
| topic | Biological Physics Dynamical Systems Pattern Formation and Solitons Quantitative Methods |
| url | https://arxiv.org/abs/2309.06339 |