Abstract

Deoxyribonucleic acid (DNA) is among the most durable chemical storage media, capable of encoding the fundamental instructions for protein synthesis (the central dogma). Each human cell contains a unique DNA sequence characterized by identifiable markers that facilitate pattern recognition. These molecular features offer significant potential for personalized drug development and disease identification. Recent advancements in DNA based research have demonstrated that fundamental arithmetic operations can be executed directly through molecular interactions, bypassing the need for silicon-based computational assistance. These biochemical applications can be further scaled to support Binary Neural Network (BNN) models, which are particularly well-suited for mitigating stochastic noise at the molecular level. In recent years, clinical studies have established that as tumor cell populations expand, the shedding of circulating tumor DNA (ctDNA) into the bloodstream increases proportionally. By leveraging Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, specifically the CRISPR-Cas9 and dCas9 systems, a diagnostic framework can be implemented to quantify ctDNA concentrations. This concentration is linearly correlated with tumor stage and size, enabling the identification of tumor origin for diagnostic purposes. By integrating Lab-on-a-Chip (LoC) architecture with molecular competition logic, a fully connected Neural Network architecture is established using a novel neuron definition. This system redefines existing molecular competition motifs through the CRISPR-Cas9 system to accommodate larger DNA sequence sizes, significantly enhancing the robustness and biocompatibility of the diagnostic platform. This work demonstrates the system definition, computer-based simulation, the neural network architecture design, and the implementation of CRISPR-based molecular competition.

Publication Date

4-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering

College

Kate Gleason College of Engineering

Advisor

Amlan Ganguly

Advisor/Committee Member

Blanca Lapizco-Encinas

Advisor/Committee Member

Cory Merkel

Comments

This thesis has been embargoed. The full-text will be available on or around 5/3/2027.

Campus

RIT – Main Campus

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