FET: Small: DNA-based Neural Networks That Learn From Their Environment

点击翻译按钮获取中文摘要

The fundamental advantage of DNA circuits, in comparison to electronic circuits, is their capability to detect and act on information in a molecular environment. A significant challenge in engineered molecular systems, DNA circuits included, is embedded learning and adaptive behavior, which is proven to be powerful and pervasive in biology and is promised to open up many doors in molecular technologies. Currently, once built, a DNA circuit always has a fixed function for how to respond to the environment, which means the same input will always trigger the same output. Some DNA circuits are reconfigurable, for example DNA neural networks, but only in the sense that a human user can choose to mix different molecules with desired concentrations for making circuits that perform different tasks. Much effort has been devoted to the design of DNA circuits with embedded learning capabilities. However, successful experimental demonstration has so far been lacking. In this project, the team of researchers will establish new circuit architectures for self-reconfigurable DNA neural networks, and show that a molecular circuit can improve how well it performs a task in a test tube without human intervention. This kind of self-improvement through learning, both supervised and unsupervised, will allow synthetic molecular circuits to gain the adaptive power previously only seen in biology, laying out the foundation for future applications in smart medicine and materials. Moreover, the results will provide experimental evidence for a pure chemical system to spontaneously undergo self-improvement, which will support the hypothesis for learning to guide and effectively accelerate evolution at the origins of life. The scientific understanding will be incorporated into open online software tools, making it accessible to general public and promoting the applications of information-processing molecular circuits. Design principles and wet-lab constructions of DNA neural networks will be introduced into the classroom, and course materials will be shared outside of the researchers' home institution. Students and postdocs will be engaged in interdisciplinary research, with an emphasis to involve more women in science. Lab tours will be provided to local college students, including underrepresented groups. Communications with general public will be facilitated by public talks, news stories, and artistic illustrations and animations of the research. The function of winner-take-all DNA neural networks depends on the concentrations of the weight molecules, which encode the memories that an input pattern is compared with for classifying the pattern. In this project, the weight molecules are designed to be initially inactive, and an appropriate collection of them will become activated when a training pattern and a label strand (indicating which class the pattern is) are simultaneously present in supervised learning. The weight-activation process will be implemented using allosteric toehold strand displacement reactions. Over the course of learning, different sets of input strands representing different training patterns will be sequentially added to the test tube that contains a DNA neural network. Each set of input strands will trigger a response of the neural network to adjust its weights and thus improve its capability for recognizing similar patterns. In unsupervised learning, the DNA neural networks are capable of restoring desired concentrations of circuit components after each round of computation and adjusting the concentrations of active weight molecules based on the circuit output rather than a given class label. The DNA neural networks will be trained and tested using a well-defined and understood task, handwritten digit recognition, to evaluate the complexity and diversity of molecular patterns that a DNA neural network is capable of learning and processing. A software tool will be developed for the design and analysis of DNA neural networks with learning capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.