Bioengineering, California Institute of Technology, Pasadena, California 91125, United States.
Computer Science, California Institute of Technology, Pasadena, California 91125, United States.
J Am Chem Soc. 2022 Jul 13;144(27):12443-12449. doi: 10.1021/jacs.2c04325. Epub 2022 Jul 2.
Molecular circuits capable of processing temporal information are essential for complex decision making in response to both the presence and history of a molecular environment. A particular type of temporal information that has been recognized to be important is the relative timing of signals. Here we demonstrate the strategy of temporal memory combined with logic computation in DNA strand-displacement circuits capable of making decisions based on specific combinations of inputs as well as their relative timing. The circuit encodes the timing information on inputs in a set of memory strands, which allows for the construction of logic gates that act on current and historical signals. We show that mismatches can be employed to reduce the complexity of circuit design and that shortening specific toeholds can be useful for improving the robustness of circuit behavior. We also show that a detailed model can provide critical insights for guiding certain aspects of experimental investigations that an abstract model cannot. We envision that the design principles explored in this study can be generalized to more complex temporal logic circuits and incorporated into other types of circuit architectures, including DNA-based neural networks, enabling the implementation of timing-dependent learning rules and opening up new opportunities for embedding intelligent behaviors into artificial molecular machines.
能够处理时间信息的分子电路对于响应分子环境的存在和历史做出复杂决策至关重要。已经认识到一种特别重要的时间信息是信号的相对定时。在这里,我们展示了基于 DNA 链置换电路的时间记忆与逻辑计算相结合的策略,该电路能够根据输入的特定组合及其相对定时做出决策。该电路将输入的定时信息编码在一组记忆链中,从而构建了可以对当前和历史信号进行操作的逻辑门。我们表明,可以使用不匹配来简化电路设计的复杂性,并且缩短特定的结合位可以有助于提高电路行为的鲁棒性。我们还表明,详细模型可以为指导实验研究的某些方面提供关键的见解,而抽象模型则无法提供这些见解。我们设想,本研究中探索的设计原则可以推广到更复杂的时间逻辑电路,并整合到其他类型的电路架构中,包括基于 DNA 的神经网络,从而实现与时间相关的学习规则,并为将智能行为嵌入人工分子机器中开辟新的机会。