Research
Enabling eco-friendly biological cybernetics for biotechnology: Advancing from biocomputing to biological AI.
We advance carbon-neutral, batteryless, and beyond-silicon communication and computing technologies for bioengineering, precision medicine, and pharmacology. Utilizing artificial intelligence, we design, develop and control technologies that enable the seamless integration of biohybrid, synthetic and natural biological systems.
Funding History
Biocomputing with living systems: lowering energy costs in computing
Controlling tissue communication allows us to explore novel activity patterns, leading to new tissue behaviors. From this, we propose biocomputing, where cell communication can be optimized to perform Boolean logic gates. We have successfully achieved logic gate functions in astrocytes, neurons, and bacteria using various cell types. Our research includes both in-silico and in-vitro models demonstrating this technology’s feasibility. We have developed control and optimization models, ranging from mathematical derivations in control theory to machine learning-based optimizations.Our future plans involve extensive exploration to advance biological computing tasks possible through this approach and identifying the biological parameters necessary for fully optimized biocomputing systems. We will also expand our analysis to in-vitro models to explore the potential sensing and treatment technologies emerging from biocomputing systems, aiming to provide more efficient and biocompatible solutions than current biomedical devices..
Data-Driven 3D Bioprinting for Smart, Adaptable, and Remote-Controlled Biomaterials
Data-driven 3D bioprinting is revolutionizing the development of smart, adaptable, and remote-controlled biomaterials. By leveraging advanced AI and machine learning algorithms, we design and fabricate bioprinted biomaterials tailored to individual patient needs. These biomaterials can dynamically adapt to changing biological conditions and be controlled remotely for precise therapeutic interventions. Our approach integrates environmental data from where the biomaterials will operate to devise strategies optimizing their functionality and performance. This enables the creation of personalized medical solutions that enhance patient outcomes. Our research focuses on developing biocompatible materials and ensuring their long-term stability and integration. Our goal is to push the boundaries of medical biomaterials, making them more efficient, versatile, and accessible, thereby contributing to the future of personalized medicine.
AI in Biology and Medicine: Accelerating the Modeling and Interfacing of Biological Systems
Molecular propagation, interaction, and information encoding are fundamental to the development, function, and evolution of biological and medical systems, manifesting in diverse ways. We leverage AI to enhance our understanding of these systems, addressing the challenges and limitations of experimental biology. Our goal is to improve disease detection and treatment by training AI models with limited data to inform biologists and medical professionals. We successfully develop digital twins of neurons, astrocytes, smooth muscle cells, epithelial cells, and bacteria, creating 3D+T digital reconstructions of tissues and organs. Our in-silico models are closely aligned with in-vitro experimental data to produce validated digital twin solutions. Our research spans several critical areas, including genetic information processing, gene network discovery, molecular biophysical modeling, experimental microscopic imaging analysis, and dynamical biomarker discovery. We aim to push the boundaries of AI in biology and medicine, contributing to a future where technology and healthcare converge to provide better, more accessible, and individualized medical solutions worldwide.
Wireless Communications for Biomedical Distributed Interfaces
The large-scale and long-term implantation of unconventional medical systems relies on the precise distribution of devices or externally controlled cells for specific sensing or actuation within the body. This is achievable only if these systems communicate with each other and external entities, a technology we call in-vivo networking. This concept is foundational for developing the futuristic vision of the Internet of Bio-Nano Things. We have created networking protocols for ultrasound-based communications between external and implantable devices. Our work involves manipulating ultrasound signals for communication and powering batteryless implantable systems for neural interfaces, which serve as AI-powered sensing mechanisms for biological neural networks. We are now expanding to multiple communication channels using ultrasound, optical, and RF-mmWave signals. Additionally, we are validating our models with 3D ex-vivo platforms, testing how wireless signals can be modified to overcome signal losses and impedance mismatches, aiming for high-bandwidth interfaces.