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Control Seminar

Synthetic Biology – from programming bacteria to programming stem cells

Ron WeissProfessorPrinceton University
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Synthetic biology is revolutionizing how we conceptualize and approach the engineering of biological systems. Recent advances in the field are allowing us to expand beyond the construction and analysis of small gene networks towards the implementation of complex multicellular systems with a variety of applications. In this talk I will describe our integrated computational experimental approach to engineering complex behavior in living systems ranging from bacteria to stem cells. In our research, we appropriate useful design principles from electrical engineering and other well established fields. These principles include abstraction, standardization, modularity, and computer aided design. But we also spend considerable effort towards understanding what makes synthetic biology different from all other existing engineering disciplines and discovering new design and construction rules that are effective for this unique discipline.
We will briefly describe the implementation of genetic circuits with finely-tuned digital and analog behavior and the use of artificial cell-cell communication to coordinate the behavior of cell populations for programmed pattern formation. Recent results with implementing Turing patterns with engineering bacteria will be presented. Arguably the most significant contribution of synthetic biology will be in medical applications such as tissue engineering. We will discuss preliminary experimental results for obtaining precise spatiotemporal control over stem cell differentiation. For this purpose, we couple elements for gene regulation, cell fate determination, signal processing, and artificial cell-cell communication. We will conclude by discussing the design and preliminary results for creating an artificial tissue homeostasis system where genetically engineered stem cells maintain indefinitely a desired level of pancreatic beta cells despite attacks by the autoimmune response. The system, which relies on artificial cell-cell communication, various regulatory network motifs, and programmed differentiation into beta cells, may one day be useful for the treatment (or cure) of diabetes.

Ron Weiss is an Associate Professor of Electrical Engineering at Princeton University, and also holds a faculty appointment in the Department of Molecular Biology. He received his PhD from the Massachusetts Institute of Technology in Computer Science and Electrical Engineering (2001). His research focuses primarily on synthetic biology, where he programs cell behavior by constructing and modeling biochemical and cellular computing systems. A major thrust of his work is the synthesis of gene networks that are engineered to perform in vivo analog and digital logic computation. He is also interested in programming cell aggregates to perform coordinated tasks using cell-cell communication with chemical diffusion mechanisms such as quorum sensing. He has constructed and tested several novel in vivo biochemical logic circuits and intercellular communication systems. Weiss is interested in both hands-on experimental work and in implementing software infrastructures for simulation and design work. For his work in Synthetic Biology, Weiss has received MIT's Technology Review Magazine's TR100 Award ("top 100 young innovators" , 2003), was selected as a speaker for the National Academy of Engineering's Frontiers of Engineering Symposium (2003), received the E. Lawrence Keyes, Jr./Emerson Electric Company Faculty Advancement Award at Princeton University (2003), his research in synthetic biology was named by MIT's Technology Review Magazine as one of " 10 emerging technologies that will change your world" (2004), was chosen as a finalist for the World Technology Network’s Biotechnology Award (2004), and was selected as a speaker for the National Academy of Sciences Frontiers of Science Symposium (2005). During recent years, Weiss has had several major publications in journals such as Nature, Nature Biotechnology, and PNAS.

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Eaton, Ford, General Motors, Toyota, Whirlpol and the MathWorks