Nanoscience and Microsystems ETDs

Publication Date

Summer 7-31-2022

Abstract

Nanotechnology promises to revolutionize many areas of applied science including materials, synthetic biology, and medicine. Devices may consist of solution-phase information processing systems or molecular robots. Of particular interest are DNA-based systems due to their composability and relatively simple interactions that are suited to bottom-up design. This work introduces high-level designs of robust control mechanisms for nanoscale devices with immediate applications in molecular computing, synthetic biology, and DNA robotics.

Nanoscale systems are dominated by probabilistic chemical behavior, so engineers must take careful consideration to produce predictable systems. Probabilistic behavior in chemical reaction networks (CRNs) yields deterministic evolution of species concentrations in systems with sufficiently large numbers of chemicals, which can be used to emulate precise mathematical functions of variables whose values are encoded as concentrations. In part, this work presents a CRN that implements a simple neural network that autonomously approximates supervised backpropagation learning, which can be used for innumerable chemical learning applications. In contrast, systems with low molecular counts experience exaggerated effects of stochasticity, which is common in biological and synthetic chemical systems. For example, as genes are expressed in individual cells, there may be on the order of hundreds or thousands of a given species; a molecular walking robot may have as few as one leg and two possible locations for its next step. This produces nondeterministic dynamics and unique design problems. To address nanoscale robotics, we explore the effect of geometrical constraints on teams of coupled surface-bound random walkers to increase their propensity for directed motion. Finally, this work presents CRNs capable of population-level consensus in stochastic systems, producing finite system states with tunable control over state choices and transitions.

Keywords

chemical reaction network, molecular computing, nanotechnology, DNA

Document Type

Dissertation

Language

English

Degree Name

Nanoscience and Microsystems

Level of Degree

Doctoral

Department Name

Nanoscience and Microsystems

First Committee Member (Chair)

Matthew R. Lakin

Second Committee Member

Darko Stefanovic

Third Committee Member

Lydia Tapia

Fourth Committee Member

William Bricker

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