Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication and other fields. Natural biochemical systems are typically modeled by chemical reaction networks (CRNs), and CRNs can be used as a specification language for synthetic chemical computation. In this paper, we identify a class of CRNs called non-competitive (NC) whose equilibria are absolutely robust to reaction rates and kinetic rate law, because their behavior is captured solely by their stoichiometric structure. Unlike prior work on rate-independent CRNs, checking non-competition and using it as a design criterion is easy and promises robust output. 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 (IRIS and MNIST), as well as tasks better aligned with potential biological applications including virus detection and spatial pattern formation.
翻译:自然生化系统通常由化学反应网络(CRNs)建模,而CRNs则可以用作合成化学计算的一种规格语言。在本文中,我们确定了一类CRNs, 其平衡性绝对强于反应率和动能率法,因为它们的行为完全取决于其系统测量结构。与以前关于依赖比率的CRNs的工作不同,检查非竞争和使用它作为设计标准很容易,并有望产生强有力的产出。我们还提出一种技术,用有根有据的深层次学习方法来编程NCNC-CRNs, 显示从修正的线性单元(ReLU)神经网络到NCR-CRNs的翻译程序。在二进制重量 ReLU网络中,我们的翻译程序非常紧凑密,因为单一的分子反应与单一的ReLU节点和反向的计算方法不同,检查不相容性标准是容易的。这个缩缩略性理论网络的编程比我们更精确的模型,我们通过经过研究的CRISM原则网络的模型来验证。