Department of Mathematics, University College London, London WC1E 6BT, UK.
Institute for Women's Health, University College London, London WC1E 6BT, UK.
Sensors (Basel). 2022 Aug 7;22(15):5907. doi: 10.3390/s22155907.
The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.
合成生物学的发展使生物技术取得了巨大的进展，并从全新的视角来研究问题。特别是，在体外、体内和计算机上设计和研究基因调控网络，在理解和控制生物现象方面发挥了越来越不可或缺的作用。在这些研究中，了解关联学习如何在分子电路层面形成是非常有趣的。数学模型越来越多地被用于预测分子电路的行为。Fernando 的模型是该研究领域中最早使用 Hill 方程的工作之一，该模型试图设计一个模仿神经网络架构中海伯学习的合成电路。在本文中，我们对该模型进行了深入的计算分析，并证明通过选择适当的参数值可以实现强化效果。我们还构建了一个新的电路，可以证明强制分离，这在 Fernando 的模型中没有观察到。我们的工作可以为考虑在生物系统中实现这类电路的合成生物学家提供参考。
