Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
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2020-03
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通讯作者：
Marko Vasić;Cameron T. Chalk;S. Khurshid;D. Soloveichik
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作者：
Marko Vasić;Cameron T. Chalk;S. Khurshid;D. Soloveichik
与传统电子学不兼容的分子嵌入计算有望在合成生物学、医学、纳米制造等领域产生广泛的影响。一个关键的挑战在于为分子计算开发编程范式，这些编程范式与底层化学硬件很好地结合在一起，而不是试图强行采用不合适的电子范式。我们发现在一类流行的神经网络（二元权重ReLU，又名BinaryConnect）和一类对反应速率绝对稳健的耦合化学反应之间存在着惊人的紧密联系。速率无关化学计算的鲁棒性使其成为生物工程实现的一个有希望的目标。我们展示了如何使用良好的深度学习优化技术在硅上训练BinaryConnect神经网络，可以编译成等效的化学反应网络，提供了一种新的分子编程范例。我们在典型的IRIS和MNIST数据集上说明了这种翻译。为了化学计算的预期应用，我们进一步使用我们的方法来生成一个化学反应网络，该网络可以根据基因表达水平区分不同的病毒类型。我们的工作为神经网络和分子编程社区之间丰富的知识转移奠定了基础。
Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields. A key remaining challenge lies in developing programming paradigms for molecular computation that are well-aligned with the underlying chemical hardware and do not attempt to shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. The robustness of rate-independent chemical computation makes it a promising target for bioengineering implementation. We show how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network, providing a novel molecular programming paradigm. We illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward intended applications of chemical computation, we further use our method to generate a chemical reaction network that can discriminate between different virus types based on gene expression levels. Our work sets the stage for rich knowledge transfer between neural network and molecular programming communities.