Matas-Gil Antonio, Endres Robert G
Department of Life Sciences & Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2BU, UK.
iScience. 2024 Apr 29;27(6):109822. doi: 10.1016/j.isci.2024.109822. eCollection 2024 Jun 21.
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.
扩散驱动的图灵不稳定性是众多生物和化学系统中空间模式形成的一种潜在机制。然而，设计这些模式并证明它们是由这种机制产生的具有挑战性。为了解决这个问题，我们旨在解决人工和实验性图灵模式中的逆问题。这项任务具有挑战性，因为模式常常被噪声破坏，并且初始条件的微小变化可能导致不同的模式。我们既使用最小二乘法来探索这个问题，也使用物理信息神经网络来构建一种抗噪声方法。我们在模拟生物噪声水平的场景中阐明了我们网络的功能，并使用实验获得的化学模式展示了其应用。研究结果揭示了机器学习在引导生物工程中合成模式创建方面的巨大潜力，从而在认识到现有局限性的同时，推进我们对生物系统中形态复杂性的理解。
