Embedding computation in biochemical environments incompatible with traditional electronics is expected to have a wide-ranging impact in synthetic biology, medicine, nanofabrication, and other fields. Natural biochemical systems are typically modeled by chemical reaction networks (CRNs) which can also be used as a specification language for synthetic chemical computation. In this paper, we identify a syntactically checkable class of CRNs called noncompetitive (NC) whose equilibria are absolutely robust to reaction rates and kinetic rate law, because their behavior is captured solely by their stoichiometric structure. In spite of the inherently parallel nature of chemistry, the robustness property allows for programming as if each reaction applies sequentially. We also present a technique to program NC-CRNs using well-founded deep learning methods, showing a translation procedure from rectified linear unit (ReLU) neural networks to NC-CRNs. In the case of binary weight ReLU networks, our translation procedure is surprisingly tight in the sense that a single bimolecular reaction corresponds to a single ReLU node and vice versa. This compactness argues that neural networks may be a fitting paradigm for programming rate-independent chemical computation. As proof of principle, we demonstrate our scheme with numerical simulations of CRNs translated from neural networks trained on traditional machine learning datasets, as well as tasks better aligned with potential biological applications including virus detection and spatial pattern formation.

中文翻译：

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编程和训练与速率无关的化学反应网络

在与传统电子不相容的生化环境中嵌入计算有望在合成生物学、医学、纳米制造和其他领域产生广泛的影响。自然生化系统通常由化学反应网络 (CRN) 建模，它也可以用作合成化学计算的规范语言。在本文中，我们确定了一种语法可检查的 CRN 类，称为非竞争性 (NC)，其平衡对反应速率和动力学速率定律绝对稳健，因为它们的行为仅由其化学计量结构捕获。尽管化学本质上具有并行性，但稳健性允许编程，就好像每个反应都按顺序应用一样。我们还提出了一种使用有根据的深度学习方法对 NC-CRN 进行编程的技术，显示从整流线性单元 (ReLU) 神经网络到 NC-CRN 的翻译过程。在二进制权重 ReLU 网络的情况下，我们的翻译过程非常紧凑，因为单个双分子反应对应于单个 ReLU 节点，反之亦然。这种紧凑性表明神经网络可能是编程与速率无关的化学计算的合适范例。作为原理证明，我们通过从在传统机器学习数据集上训练的神经网络翻译而来的 CRN 的数值模拟，以及与潜在生物学应用（包括病毒检测和空间模式形成）更好地结合的任务来展示我们的方案。我们的翻译过程非常紧凑，因为单个双分子反应对应于单个 ReLU 节点，反之亦然。这种紧凑性表明神经网络可能是编程与速率无关的化学计算的合适范例。作为原理证明，我们通过从在传统机器学习数据集上训练的神经网络翻译而来的 CRN 的数值模拟，以及与潜在生物学应用（包括病毒检测和空间模式形成）更好地结合的任务来展示我们的方案。我们的翻译过程非常紧凑，因为单个双分子反应对应于单个 ReLU 节点，反之亦然。这种紧凑性表明神经网络可能是编程与速率无关的化学计算的合适范例。作为原理证明，我们通过从在传统机器学习数据集上训练的神经网络翻译而来的 CRN 的数值模拟，以及与潜在生物学应用（包括病毒检测和空间模式形成）更好地结合的任务来展示我们的方案。