Research
Advancing Biocomputing and Neuroengineering for Biomedical Technology: From Biohybrids to Living System Interfaces
My research explores the intersection of biocomputing, neuroengineering, and synthetic biology to develop biohybrid systems that integrate engineered cellular networks with biomedical technology. By leveraging AI-driven modeling, predictive simulations, and biofabrication, my work seeks to understand, interface with, and augment biological computation at multiple scales. By investigating cellular decision-making processes, my work explores adaptive biological circuit designs with potential for scalable applications. I develop proof-of-concept models demonstrating innovations in neural tissue regeneration, exosome-based drug delivery, and biologically inspired computational frameworks, bridging the gap between engineered biology and clinical applications. Through the integration of bioprinting platforms and genetically engineered cellular networks, my research facilitates dynamic, distributed computational processes within biological environments. These advancements lay the foundation for next-generation therapeutic platforms, personalized regenerative medicine, and bioelectronic interfaces, while ensuring rigorous safety and ethical oversight.
Key Areas
Controlling tissue communication enables the discovery of novel activity patterns, unlocking new possibilities for engineered biological computation. My research proposes biocomputing as a framework for leveraging cellular signaling pathways to perform computational tasks, ranging from Boolean logic circuits to biologically inspired processing models. Through a combination of in-silico simulations and in-vitro experiments, we have demonstrated biologically inspired logic circuits in astrocytes, neurons, and bacterial systems. This approach supports the development of bio-computational frameworks that integrate control theory with machine learning-based optimization strategies. Moving forward, I aim to identify critical biological parameters that enhance the reliability and scalability of biocomputing platforms. Additionally, this research investigates novel sensing and therapeutic applications derived from biocomputing principles, fostering adaptive, bio-compatible biomedical technologies with real-world clinical potential.
4D bioprinting enables programmable, adaptive biomaterials that respond dynamically to biological and environmental stimuli. My research integrates AI-driven design optimization with advanced fabrication techniques to develop biohybrid materials for regenerative medicine and tissue engineering. By incorporating biological and environmental data, we refine bioprinting strategies to enhance the functionality and adaptability of these biomaterials. These materials are designed to undergo controlled transformations over time, responding to biochemical, mechanical, or external stimuli to improve integration with living systems. Beyond fabrication, this work aims to establish long-term biocompatibility and stability of biohybrids, ensuring their effective application in personalized medicine, therapeutic delivery, and engineered tissue systems.
Understanding molecular interactions, cellular signaling, and biophysical dynamics is crucial for advancing biomedical research and precision medicine. My research harnesses AI-driven computational modeling to analyze these complex biological processes, helping our understanding of complex biological dynamic activity. A core focus is the development of biological digital twins with high-fidelity, computational reconstructions of tissues and organ systems, including neurons and astrocytes networks, microcircuits and microlumns. These models are iteratively validated against in-vitro experimental data, allowing for real-time simulation and predictive modeling of physiological behaviors. We strive to bridge in-silico simulations with experimental validation to advance personalized healthcare solutions and bioengineered medical applications.
The successful implementation of large-scale, long-term biohybrid systems relies on the precise coordination and distribution of devices or externally controlled cells for targeted sensing and actuation within biological environments. My research focuses on advancing wireless communication frameworks, enabling these biohybrid systems to interact seamlessly with each other and external entities—a concept foundational to the vision of the Internet of Bio-Nano Things. We have developed and refined networking protocols for ultrasound-based communication, facilitating interactions between external controllers and implantable devices. This includes powering batteryless implantable systems that serve as AI-driven neural interfaces, capable of functioning as high-precision sensing mechanisms for biological neural networks. Our work now explores integrating multiple communication modalities, such as ultrasound, optical, and RF-mmWave signals, to create robust and efficient multi-channel systems. By validating these models with 3D ex-vivo platforms, we aim to overcome challenges like signal loss and impedance mismatches, pushing the boundaries of high-bandwidth, biocompatible wireless interfaces. This research is paving the way for the next generation of biohybrid technologies with applications in regenerative medicine, neural interfacing, and beyond.
