Research in my lab is at the interface of computation, engineering, and medicine. We apply engineering tools and principles, such as control theory, information theory, and reaction kinetics, to guide reprogramming of cellular behavior using synthetic gene circuits. In the long term, these synthetic gene circuits will find diverse applications, including novel systems for protein engineering, metabolic engineering, drug development, gene therapy, and stem cell reprogramming.


However, programming of predictable cellular behavior is challenging. Unlike typical engineered systems, cellular processes are intrinsically noisy. In each cell, gene expression is subject to stochastic fluctuations due to small numbers of interacting molecules, heterogeneity of intracellular environments, and extra-cellular perturbations. Presence of cellular noise raises two fundamental questions. First, how is nature able to assemble noisy, imperfect components into robust systems that accurately carry out their functions? Second, as bioengineers, how can we design synthetic gene circuits that will function reliably despite cellular noise?


My lab approaches these questions from two complementary perspectives. First, we combine mathematical modeling and experiments to analyze dynamics of natural signaling processes, including cell cycle regulation and cell-cell communication. Second, based on insights learnt from these natural systems, we design and construct synthetic gene circuits with well-defined functions. Implementation of these gene circuits will also enable us to probe biological design strategies in a well-defined framework, and to generate systems with applications in medicine and biotechnology. Specifically, we are pursuing three highly synergistic directions:


Programming bacterial population dynamics: These projects aim to engineer synthetic gene circuits that can precisely program bacterial growth, death, and aggregation in complex environments, with implications for therapeutic and biotechnology applications.


Cellular information processing: These projects aim to define characteristics of information processing in the presence of cellular noise. A long term goal is to engineer gene circuits to perform cell-based computation.


Analysis and reprogramming of mammalian cell cycle: These projects aim to analyze and perturb mammalian cell cycle regulation by modeling. Their will provide insight into development of strategies to reprogram and interfere with cell cycle regulation for cancer therapy.