Recurrent neural chemical reaction networks that approximate arbitrary dynamics

Alexander Dack, Benjamin Qureshi, Thomas E. Ouldridge, Tomislav Plesa
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Abstract

Many important phenomena in chemistry and biology are realized via dynamical features such as multi-stability, oscillations, and chaos. Construction of novel chemical systems with such finely-tuned dynamics is a challenging problem central to the growing field of synthetic biology. In this paper, we address this problem by putting forward a molecular version of a recurrent artificial neural network, which we call a recurrent neural chemical reaction network (RNCRN). We prove that the RNCRN, with sufficiently many auxiliary chemical species and suitable fast reactions, can be systematically trained to achieve any dynamics. This approximation ability is shown to hold independent of the initial conditions for the auxiliary species, making the RNCRN more experimentally feasible. To demonstrate the results, we present a number of relatively simple RNCRNs trained to display a variety of biologically-important dynamical features.
近似任意动力学的递归神经化学反应网络
化学和生物学中的许多重要现象都是通过动力学特征实现的,例如多稳态、振荡和混沌。构建具有这种微调动态特性的新型化学系统是一个极具挑战性的问题,也是不断发展的合成生物学领域的核心问题。在本文中,我们提出了一种分子版本的递归人工神经网络,称之为递归神经化学反应网络(RNCRN),从而解决了这一问题。我们证明,只要有足够多的辅助化学物种和合适的快速反应,就可以对 RNCRN 进行系统训练,使其达到任何动力学水平。这种近似能力与辅助物种的初始条件无关,这使得 RNCRN 在实验上更加可行。为了证明这些结果,我们展示了一些经过训练的相对简单的 RNCRN,它们显示了各种生物学上重要的动力学特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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