Sequestration-based neural networks that operate out of the equilibrium

Speaker: Christian Cuba Samaniego, Carnegie Mellon University

Classification of high-dimensional information is a ubiquitous computing paradigm across diverse biological systems, including organs such as the brain, down to signaling between individual cells. Inspired by the success of artificial neural networks in machine learning, the idea of engineering genetic circuits that operate as neural networks emerges as a strategy to expand the classification capabilities of living systems. In this work, we design these biomolecular neural networks (BNNs) based on the molecular sequestration reaction, and experimentally characterize their behavior as linear classifiers for increasing levels of complexity.

Event Details

• Date: Monday, October 6
• Time: 11:30 a.m. – 12:30 p.m.
• Location: BST3 6014

About the Speaker

Christian Cuba Samaniego is an Assistant Professor in the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University. His research lies at the intersection of synthetic biology, control theory, and computational biology, with a focus on engineering biomolecular systems that can perform complex tasks such as pattern recognition and feedback control within living cells. Prior to joining CMU, he was a research fellow in the Department of Immunology at the Dana-Farber Cancer Institute and Harvard Medical School, and held postdoctoral positions at MIT and UCLA. He earned his Ph.D. in control theory applications in synthetic biology from the University of California, Riverside. Originally from Peru, he completed his undergraduate studies in Mechatronics Engineering at the National University of Engineering in Lima.