解析生物化学空间模式：用于Turing模式逆问题的机器学习方法
Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns
AI总结：
本文提出利用机器学习方法解决Turing模式的逆问题，通过抗噪声神经网络在生物噪声环境下生成合成模式，推动生物工程和形态学研究。
摘要：
扩散驱动的Turing不稳定性是许多生物和化学系统中空间模式形成的潜在机制。然而，工程这些模式并证明它们是由这种机制产生的具有挑战性。为此，我们旨在解决人工和实验Turing模式中的逆问题。这一任务具有挑战性，因为高水平的噪声会破坏这些模式，且初始条件的微小变化可能导致不同的模式。我们使用最小二乘法来探索该问题，并利用物理信息神经网络构建一种抗噪声的方法。我们阐述了我们的网络在模拟生物噪声水平的场景中的功能，并通过一个涉及实验获得的化学模式的原型展示了其应用。研究结果揭示了机器学习在生物工程中引导合成模式创建中的巨大潜力，从而推动我们对生物系统形态细节的理解，同时承认现有局限性。
英文摘要：
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.
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