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News and Updates

2025

[August] We received a NSF CCF FET EAGER grant titled "Closed-loop Bioelectronic Control of Computing through Bacterial Gene Regulatory Artificial Neural Networks" in collaboration with the Atkinson Lab at Princeton University. Thank you NSF for your generous and continued support of our research!
[June] Congratulations to Jacob Clouse on receiving the David A. Klarner Fellowship in Computer Science!
[June] Congratulations to Sehee Sun for successfully defending her MS thesis titled "Biocomputing Approach to Modeling and Modulating Calcium Signaling"!
[June] Our paper "The Internet of Biofilm Living AI Devices" has been accepted for IEEE Communications Magazine.
[March] Our paper "Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence" has been accepted for IEEE Intelligent Systems.
[March] Our paper "Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation" has been accepted for IEEE Transactions on Network Science and Engineering.
[February] Our paper "Analyzing Wet-Neuromorphic Computing Using Bacterial Gene Regulatory Neural Networks" has been accepted for IEEE Transactions on Emerging Topics in Computing.

2024

[September] Our paper titled “Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network” is published in Biophysical Reports. Paper
[June] LDS organizes panel on “Nanonetworking; Quo Vadis?” at IEEE ICC.
[March] Hackster.io featured our latest study titled "Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity". Thanks, Hackster.io!

2023

[November] Our IEEE Network paper is featured in IEEE Spectrum
[September] LDS received CBET NSF US-Ireland R&D partnership jointly funded jointly funded by the NSF Biosensing Program and EPSCoR (NSF CBET 2316960). Thank you NSF for your generous support!

Research

Microbiome Digital Twin

In the 21st century, understanding the evolution of microbial ecology is crucial amid emerging threats to health and the environment, exacerbated by bacterial species evolving and transforming their behavior over time. This research aims to develop a microbiome digital twin integrating intra-cellular signaling models from trans-omic data, facilitating simulation of molecular propagation and inter-cellular communication within bacterial populations. The digital twin, linked to wild-type cells via electrochemical nanosensors and engineered biosensors, could establish an open platform for global bacterial tracking, environmental monitoring, and medical diagnostics through miniature bio-hybrid implantable devices.

Wet-Neuromorphic Computing for Biological AI

In recent years, the proliferation of Artificial Intelligence and Machine Learning has led to the development of diverse software and hardware systems, from artificial neural networks to neuromorphic processors, with algorithms ranging from deep learning to lightweight models. Our research aims to explore a novel approach to AI by harnessing living cells, particularly through the creation of Gene Regulatory Neural Networks (GRNNs) based on natural neural network structures found in biological cells. Our recent findings demonstrate the feasibility of implementing image detection computing in bacterial A-GRNN subnetworks, highlighting the potential of packing dense neural network-like structures into single bacterial cells. This research is motivated by the need to address the energy demands of AI algorithms and the opportunity to integrate AI into non-silicon devices.

Terahertz Communications and Internet of Nano Things

As the telecommunications industry transitions to 5G, research efforts are already underway to explore the possibilities of 6G, particularly in the upper millimeter-wave and terahertz spectrum, offering unprecedented data rates of up to terabit-per-second (Tbps). This research delves into the potential applications of terahertz frequencies for both communication and sensing, including atmospheric gas measurement and the development of terahertz nanonetworks for building infrastructure sensing.

Selected Publications

- Perera, J., Balasubramaniam, S., Somathilaka, S., Wen, Q., Li, X., Kasthurirathna, D., Roohi, A., Nelson, T. S., “Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence”, IEEE Intelligent Systems, (accepted), 2025.
- A. Ratwatte, S. Somathilaka, S. Balasubramaniam, M. Taggart, K. M. Nair, A. O’Riordan, J. Dooley, “Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation”, IEEE Transactions on Network Science and Engineering (accepted), 2025.
- Somathilaka, S., Balasubramaniam, S., Martins, D. P., “Analyzing Wet-Neuromorphic Computing Using Bacterial Gene Regulatory Neural Networks”, IEEE Transactions on Emerging Topics in Computing (accepted), 2025.
- Ratwatte, A., Somathilaka, S., Balasubramaniam, S., Gilad, A. A., “Non-Linear Classifiers for Wet-Neuromorphic Computing using Gene Regulatory Neural Network", Biophysical Reports, vol. 4, no. 11, 2024.
- Akyildiz. I. F., Pierobon, M., Balasubramaniam, S., “An Information Theoretic Framework to Analyze Molecular Communication Systems Based on Statistical Mechanics”, Proceedings of IEEE, vol. 107, no. 7, July 2019.

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LAB LOCATION

School of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska

News and Updates

Research

Microbiome Digital Twin

Wet-Neuromorphic Computing for Biological AI

Terahertz Communications and Internet of Nano Things

Selected Publications

Current and Past Sponsors

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