Ahmad (Mo) Khalil

Ahmad (Mo) Khalil

Assistant Professor
Boston University

Ahmad (Mo) Khalil is Assistant Professor of Biomedical Engineering and the Founding Associate Director of the Biological Design Center at Boston University. He is also a Visiting Scholar at the Wyss Institute for Biologically Inspired Engineering at Harvard University. His research is interested in how molecular circuits enable cellular functions, such as decision-making, computation, and epigenetic memory. His team applies synthetic biology and other multidisciplinary approaches to study and manipulate the function and evolution of these cellular systems. He is recipient of numerous awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE), NIH New Innovator Award, NSF CAREER Award, DARPA Young Faculty Award, and the Hartwell Foundation Biomedical Research Award, and has received numerous awards for teaching excellence at both the Department and College levels. Mo was an HHMI Postdoctoral Fellow with Dr. James Collins at Boston University. He obtained his Ph.D. from MIT and his B.S. (Phi Beta Kappa) from Stanford University.

Research

My team is interested in how molecular circuits enable cellular functions, such as decision-making, computation, and epigenetic memory. We apply synthetic biology and other multidisciplinary approaches to study and manipulate the function and evolution of these cellular systems. Current studies focus on developing synthetic biology approaches to illuminate and control eukaryotic transcriptional regulation, chromatin/epigenetic regulatory systems, and protein aggregation systems. In parallel, we have invented technologies, such as the “eVOLVER", a do-it-yourself (DIY) platform that enables researchers to design customized, automated, and high-throughput growth experiments to quantify cellular adaptation and perform laboratory evolution of biological systems. With these tools and approaches, our goal is to gain integrated quantitative understanding of how cellular phenotypes emerge from underlying regulatory networks, how these regulatory systems evolve, and how to predictively engineer them for biomedical and therapeutic applications.