Domitilla Del VecchioView all speakers
Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from CalTech, Pasadena, and the Laurea degree in Electrical Engineering from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor.
In 2010, she joined the Department of Mechanical Engineering and the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT), where she is currently the W. M. Keck Career Development Associate Professor in Biomedical Engineering. She is a recipient of the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the Crosby Award, University of Michigan (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000).
The ability to accurately predict a network’s behavior from that of the composing modules is a major challenge in systems and synthetic biology due to individual modules exhibiting context-dependent behavior. One leading cause of context-dependence is retroactivity, a phenomenon similar to loading that affects the dynamic performance of a module upon connection to other modules. Retroactivity is particularly daunting for synthetic biology, in which working modules often fail to function as predicted once interacting with each other. Here, we illustrate analysis and design techniques to mitigate this problem along with experimental demonstrations. First, we introduce a simple analysis framework, conceptually analogous to Thevenin’s electrical circuit theory, which accounts for retroactivity to quantitatively predict how a module’s behavior will change after interconnection with other systems. Experiments carried both in yeast and E. coli validate this theory and demonstrate that retroactivity substantially deteriorates the dynamic response of gene circuits. To solve this problem, we introduce a load driver device to connect a circuit to a variable number of systems (load) in such a way that the circuit output is reliably transmitted to the load while preserving the unloaded system performance. The load driver design is based on fast regulatory elements that reach quasi-steady state quickly in response to slow changing inputs (principle of time-scale separation). These fast elements are in sufficiently high concentrations such that the quasi-steady state is unaffected by load. We built and experimentally tested in Saccharomyces cerevisae an instance of a load driver with circuitry based on (fast) phosphotransfer reactions. We first built and tested several transcriptional regulatory networks and demonstrated that their responses were severely impaired due to load. We then demonstrated that incorporation of our load driver restored response dynamics, thus remedying the load problem.