Exploring Gene Regulatory Neural Network Biocomputing of Bacteria

Authors

• Adrian Merle Ratwatte, University of Nebraska-LincolnFollow

First Advisor

Sasitharan Balasubramaniam

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

Byrav Ramamurthy, Nirnimesh Ghose, Xu Li

Department

Computer Engineering

Date of this Version

6-23-2026

Document Type

Dissertation

Citation

A dissertation presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree Doctor of Philosophy

Major: Computer Engineering

Under the supervision of Professor Sasitharan Balasubramaniam

Lincoln, Nebraska, May 2026

Comments

Copyright 2026, Adrian Merle Ratwatte. Used by permission

Abstract

Artificial Intelligence (AI) has evolved from brain-inspired algorithms into a discipline that increasingly integrates with biological systems. While silicon-based platforms have advanced machine learning, they remain limited in energy efficiency and operation in environments beyond silicon. This motivates biological computing as an alternative, enabling efficient, resource-aware, and reconfigurable computing within living systems. This dissertation addresses these limitations by introducing a bacterial computing framework that models Gene Regulatory Networks (GRNs) as Gene Regulatory Neural Networks (GRNNs). The GRNN mirrors the structure and function of Artificial Neural Networks (ANNs) through gene-gene interactions across trans-omic layers, enabling natural, self-regulating information processing within living systems. At the cellular scale, a dual-layered chemical reaction model describes transcription and translation dynamics, revealing the sigmoidal activation behavior of genes modeled as gene-perceptrons. This abstraction enabled the isolation of sub-GRNNs from the larger GRNN that function as non-linear classifiers, where Lyapunov-based stability analysis ensured reliable computing under concentration fluctuations. Building upon this foundation, a control-theoretic framework was introduced to determine optimal chemical input concentrations that guide the GRNN toward desired weight configurations using the Linear Quadratic Regulator (LQR) approach. This control model maintained a balance between stability and reconfigurability while mitigating Clostridioides difficile biofilm formation. Extending beyond control applications, recent work transforms bacterial gene expression dynamics into a GRNN-based biocomputing library of mathematical solvers. Current biocomputing approaches mainly rely on fixed engineered circuits, limiting stability and reliability across different conditions. In contrast, this work uses the GRNN framework to identify functional subnetworks directly from native GRNs. A sub-GRNN search algorithm is developed to identify subnetworks that match task-specific gene expression patterns under chemically encoded input codes. Mathematical calculation and classification tasks, including identifying Fibonacci numbers, prime numbers, multiplication, and Collatz step counts, are used as case studies to validate the framework. The identified sub-GRNNs are evaluated using gene-wise perturbation, collective perturbation, and Lyapunov-based analysis to assess computing stability and reliability. The results show that native transcriptional dynamics can support diverse computing tasks while maintaining stable and reliable performance.

Recommended Citation

Ratwatte, Adrian Merle, "Exploring Gene Regulatory Neural Network Biocomputing of Bacteria" (2026). Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–. 509.
https://digitalcommons.unl.edu/dissunl/509