Molecular Pattern Recognition and Supervised Learning in DNA-Based Neural Networks

Citation

Cherry, Kevin Matthew (2024) Molecular Pattern Recognition and Supervised Learning in DNA-Based Neural Networks. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/529f-kf62. https://resolver.caltech.edu/CaltechTHESIS:04292024-172707491

Abstract

Adaptation in nature begins at the subcellular, molecular level with the delicate interplay of biomolecule cascades orchestrating the myriad function of cells. The intermingling activity of these cells becomes expressions of complex behavior of multi-cellular system. Nature provides a dazzling array of examples showcasing the variations of intelligent functions. However, in the realm of synthetic construction, what systems have humans managed to engineer, and what are the boundaries of our technological power? In comparison to nature's repertoire, mankind's accomplishments appear rather modest. The intricate behaviors observed in intelligent organisms emerge from the collective interactions and feedback loops among their constituent elements, resulting in the emergence of novel properties and phenomena. To develop large-scale engineered systems exhibiting ever more brain-like, intelligent behaviors, we must first devise new molecular architectures and algorithms designed for adaptation and learning at the molecular scale. My research presented here is a humble step toward those goals. I will present the design of novel molecular systems made from DNA that exhibit complex neural computation and learning behaviors.

Chapter 2 covers my contribution to scaling up the computing power of DNA circuits. From bacteria following simple chemical gradients to the brain distinguishing complex odor information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks. Winner-take-all computation has been suggested as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits and Hopfield networks used previously, winner-take-all circuits are computationally more powerful, allow simpler molecular implementation, and are not constrained by coupling the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here, we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement reactions. We use a previously developed seesaw DNA gate motif, extended to include a simple and robust component that facilitates the cooperative hybridization involved in selecting a ‘winner.' We show that with this extended seesaw motif, DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10x10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits ‘1’ to ‘9.' The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns ‘remembered’ during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.

Chapter 3 investigates the development of a computational neural network model inspired by biological learning mechanisms, particularly focusing on the new mechanisms for learning in a WTA neural network. The study incorporates novel molecular motifs used in inhibited activators and inhibited weights, designed specifically for training from environmental input patterns. These motifs emulate biological systems by facilitating memory storage and retrieval within DNA-based neural networks, similar to synaptic connections and signal processing observed in living organisms. We assess the function of the individual molecular motifs and characterize their specificity in up to 18-species cross-talk experiments. Furthermore, we characterize the network's performance across a wide array of training and test patterns, mirroring the adaptive responses and diverse conditions encountered by biological systems. Additionally, we analyze the computational efficiency and speed of the learning system, comparing it with both the previous non-learning DNA-based WTA model and a direct weight activation model. By exploring the principles of molecular learning, particularly within winner-take-all neural networks, this study aims to advance computational systems by emulating adaptability and resilience observed in biological organisms using robust, new molecular motifs.

Thesis Files