Research

Microbiome Digital Twin

In the 21st century, understanding the evolution of microbial ecology has become increasingly critical, particularly as we face emerging threats to human health and the global environment. This research aims to develop a microbiome digital twin that interfaces with living bacterial cells, enabling prediction of their internal computing and communication behaviors. The digital twin will incorporate models of internal gene regulatory computing mechanisms, enabling reconfiguration of bacterial behavior in a controlled and programmable manner. The digital twin will be interconnected with wild-type bacteria via electrochemical nanosensors (developed by Tyndall National Institute (TNI)) and engineered bacterial biosensors (developed by Ulster University (UU)), and integrated into a unified digital framework (developed by University of Nebraska–Lincoln (UNL)). This platform will lay the foundation for “bacterial citizens on the Internet,” enabling (i) novel bio-cyber environmental monitoring systems, (ii) collaborative global tracking of bacterial evolution and spread, and (iii) future medical diagnostics and treatments through miniature bio-hybrid implantable devices. Detail on our bacteriome digital twin can be found in IEEE JBHI, IEEE ICC, while the reconfiguration using internal computing in bacteria can be found in IEEE TNSE. The details on the electrochemical sensors can be found in ACS Electrochemistry.

Wet Neuromorphic Computing

We explore new forms of AI that are chemically computed through bacterial gene regulatory processes, enabling the design of future energy-efficient AI hardware beyond traditional silicon-based systems. This paradigm not only reduces reliance on conventional neuromorphic architectures but also enables deployment in environments where silicon-based systems cannot be embedded. To support this, we have developed a search algorithm that identifies functional Gene Regulatory Neural Network (GRNN) subnetworks, demonstrating their capability to perform tasks such as image recognition, regression for approximating mathematical functions, numerical computation (e.g., generating the Fibonacci sequence), and classification (e.g., detecting prime numbers). Together, these results position GRNNs as a foundation for programmable, bio-computing intelligence operating at the extreme edge. Further details on the GRNN computing can be found in IEEE TETC, Biophys. Rep., TinyML, and Biophys. Rep.. The background details on wet-neuromorphic computing can be found in IEEE Intell. Syst. and IEEE Nanotechnol. Mag., while the mechanism of interconnecting to the Internet as an AI machine at the edge in IEEE Comm. Mag..

Microfluidic Reservoir Computing

Reservoir computing has emerged as a powerful paradigm for implementing AI using the intrinsic dynamics of physical systems rather than relying solely on conventional digital processors. Inspired by this vision, this work explores a new direction in physical AI by using microfluidic chips as computational substrates. The device architecture is inspired by the complex vein networks found in insect wings and leverages the nonlinear mixing dynamics of colored liquids flowing through microchannels to form the reservoir. Temporal input signals encoded through dye injections are transformed into rich spatiotemporal patterns that are captured by imaging sensors and processed by a trainable readout layer for pattern classification. The results demonstrate that microfluidic systems can perform reliable computation, highlighting their potential as low-power and unconventional hardware platforms for future reservoir computing architectures. Further details can be found in Cell Press Device.

Terahertz Communications and Internet of Nano Things

While the telecommunications industry is rolling out 5G globally, the research community is busy researching new disruptive technologies. 6G is expected to progress into the upper millimeter-wave (100-300 GHz) and the terahertz (0.3- 10 THz) spectrum. The larger bandwidth available at THz frequencies has the potential to provide high data rates, approaching a terabit-per- second (Tbps) or more. This research aims to investigate the potential for Thz frequencies that can be used for both communications as well as sensing. One application is utilizing selected frequency ranges that are unsuitable for communication but are chosen to measure certain atmospheric gases. Another research direction is the development of Thz nanonetworks that can be embedded into building infrastructures to perform sensing. Further details can be found in IEEE JSAC, IEEE Netw., IEEE Access, and IEEE J. Sel. Top. Signal Process..

Past Research

Brain-Machine Interface and Wireless Optogenetics (selected publications)

Bernal, S. L., Celdrán, A. H., Barros, M. T., Pérez, G. M., Balasubramaniam, S., “Security in Brain-Computer Interfaces: State-of-the-art, Opportunities, and Future Challenges”, ACM Computing Survey, vol. 54, no. 1, January 2021.
Wirdatmadja, S., Jornet, J. M., Koucheryavy, Y., Balasubramaniam, S., “Channel Impulse Analysis of Light Propagation for Point-to-point Nano Communications through Cortical Neurons”, IEEE Transactions on Communications, vol. 68, no. 11, 2020.
Donoghue, M., Jennings, B., and Balasubramaniam, S., “Capacity Analysis of a Peripheral Nerve using Modulated Compound Action Potential Pulses”, IEEE Transactions on Communications, vol. 67, no. 1, 2019.
Balasubramaniam, S., Wirdatmadja, S. A., Barros, M. T., Koucheryavy, Y., Stachowiak, M., and Jornet, J. M., “Wireless Communications for Optogenetics-based Brain Stimulation: Present Technology and Future Challenges”, IEEE Communications Magazine, vol. 56, no. 7, July 2018.
Wirdatmadja, S. A., Barros, M. T., Jornet, J. M., Koucheryavy, Y., and Balasubramaniam, S., “Wireless Optogenetic Nanonetworks for Brain Stimulation: Device Model and Charging Protocols”, IEEE Transactions on Nanobioscience, vol. 16, no. 8, 2017.

Bio-inspired Communication Networks (selected publications)

Mineraud, J., Wang, L., Balasubramaniam, S., and Kangasharju, J., “Hybrid Renewable Energy Routing for ISP Network”, in Proc. of IEEE INFOCOM, San Francisco, USA, April 2016.
Balasubramaniam, S., Mineraud, J., Perry, P., Jennings, B., Murphy, L., Donnelly, W., and Botvich, D., “Coordinating Allocation of Resources for Multiple Virtual IPTV Providers to Maximise Revenue”, IEEE Transactions on Broadcasting, vol. 57, no. 4, December 2011.
Balasubramaniam, S., Botvich, D., Carroll, R., Mineraud, J., Nakano, T., Suda, T., and Donnelly, W., “Biologically Inspired Future Service Environment”, Computer Networks (Elsevier), vol. 55, no. 15, October 2011.
Balasubramaniam, S., Leibnitz, K., Lio', P., Botvich, D., and Murata, M., “Biological Principles for Future Internet Architecture Design”, IEEE Communications Magazine, vol. 49, no. 7, July 2011.
Balasubramaniam, S., Botvich, D., Mineraud, J., Donnelly, W., and Agoulmine, N., “BiRSM: Bio-inspired Resource Self-Management for All IP Networks”, IEEE Network, vol. 24, no. 3, May/June 2010.

Body Area and Sensor Networks (selected publications)

Ivanov, S., Balasubramaniam, S., Botvich, D., and Akan, O. B., “Gravity Gradient Routing for Information Delivery in Fog Wireless Sensor Networks”, Ad Hoc Networks, vol. 46, August 2016.
Ivanov, S., Foley, C., Botvich, D., and Balasubramaniam, S., “Virtual Groups for Patient WBAN Monitoring in Medical Environments”, IEEE Transactions on Biomedical Engineering, vol. 59, no. 11, November 2012.
Ivanov, S., Botvich, D., and Balasubramaniam, S., “Cooperative Wireless Sensor Environments Supporting Body Area Networks”, IEEE Transactions on Consumer Electronics, vol. 58, no. 2, May 2012.

Research Grants

“EAGER: Closed-loop Bioelectronic Control of Computing through Bacterial Gene Regulatory Artificial Neural Networks”, National Science Foundation: 1.5 Years (September 2025 – February 2027): $299,813 (Co-PI: J. Atkinson, Princeton University).
“Bacterial-based Biosensor Digital Twin for Microbial Community Sensing”, National Science Foundation US-Ireland R&D Partnership: 3 Years (September 2023 – August 2026). Amount Funded: $399,974 as Lead-PI.
“Future Artificial Intelligence through Biomimetic Living micro-Brain”, NU Collaborative Initiative grant: 2 Years (July 2022 – June 2024). Amount Funded: $150K.
“PRIME: A Personalised Living Cell Synthetic Computing Circuit for Sensing and Treating Neurodegenerative Disorders”, EU H2020 Future and Emerging Technology: 4 Years (February 2021 –January 2025). Amount Funded: €4.4 million, and €707K allocated to myself as PI and coordinator.
“GLADIATOR (Next-generation theranostics of brain pathologies with autonomous externally controllable nanonetworks: a trans-disciplinary approach with bio-nanodevice interfaces)” EU H2020 Future and Emerging Technology: 4 Years (February 2019 – January 2023). Amount Funded: €5.9 million, and €421K allocated to to myself as Co-PI.
“VistaMilk – Milk by Design” Science Foundation Ireland Research Centre: 6 Years (September 2018 – August 2024). Amount Funded: €25 million for 5 PIs, and €3 million allocated to the Walton Institute with me as Co-PI.
“FutureNeuro” Science Foundation Ireland Research Centre: 6 Years (September 2018 – August 2024). Amount funded €10 million, and €100,000 allocated to myself as Funded Investigator.
“CONNECT”, Science Foundation Ireland Research Centre: 6 Years (January 2016 – December 2020). Amount Funded: €150,000 to me as a Funded Investigator.
“Theoretical and Experimental Development of Protocols for Molecular Communications,” Academy of Finland – Academy of Finland Research Fellow, Duration: 5 Years (September 2014 – August 2019). Amount Funded: €835, 000 allocated to myself as PI.
“A Biologically Inspired Framework supporting Network Management for the Future Internet,” Science Foundation Ireland - Starter Investigator Research Grant (SIRG), Duration: 4 Years (October 2009 –September 2013). Amount Funded: €362, 291 allocated to myself as PI.
“Distributed Green Routing for Software Routers,” Science Foundation Ireland, Duration: 3 Months (March 2012 – June 2012). Amount Funded: €9, 191.
“Nano Communication in Microfluidic Devices,” Tampere University, Strategic Application, Duration: 1 Year (January 2014-December 2014). Amount Funded: €100,000.
“Federated Autonomic Management of End-to-end Services,” Science Foundation Ireland Strategic Research Cluster, Duration: 5 Years (January 2009-December 2013), Amount Funded: $70,000.
“Serving Society: Management of Future Communication Network and Services,” Higher Education Authority, Ireland, Duration: 3 Years (October 2008-September 2011). Amount Funded: $140,000.

Page updated

Google Sites

Report abuse