The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks-capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification.

中文翻译：

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由酶反应构建的神经网络可以作为线性和非线性分类器

能够处理多输入生物标志物模式的分子程序工程在从体外诊断（例如，病毒检测，包括COVID-19）到治疗干预（例如，区分癌细胞与正常细胞）的应用中具有巨大潜力。因此，设计用于模式识别的分子网络的机制受到高度追捧。在这项工作中，我们探索酶网络如何用于线性和非线性分类任务。通过利用稳态分析并显示全局稳定性，我们证明这些网络可以充当分子感知器（人工神经网络的基本单元），能够处理与正负权重相关的多个输入以实现线性分类。此外，通过组合正交酶反应，我们表明可以构建多层网络来实现非线性分类。