This is a joint AFOSR BRI project between MIT, Boston University, Caltech and Rutgers. This page primarily describes the work done in Richard Murray's group.
- Ania Baetica (CDS)
- Anandh Swaminathan (PhD student, CDS)
- Enoch Yeung (PhD student, CDS)
- Yutaka Hori* (postdoc, CDS)
- Vipul Singhal (PhD student, CNS)*
- Domitilla Del Vecchio (MIT)
- Jim Collins (BU)
- Eduardo Sontag (Rutgers)
The objective of this research is to establish a data-driven theoretical framework based on mathematics to enable the robust design of interacting biomolecular circuits in living cells that perform complex decision making. Microbiology as a platform has substantial advantages with respect to human-made hardware, including size, power, and high sensitivity/selectivity. While the latest advances in synthetic biology have rendered the creation of simple functional circuits in microbes possible, our ability of composing circuits that behave as expected is still missing. This hinders the possibility of designing robust complex decision making, including recognition and classification of chemical signatures. Overcoming this bottleneck goes beyond the engineering of new parts or new assembly methods. By contrast, it requires a deep understanding of the dynamical interactions among synthetic modules and the cell machinery, a particularly hard task since dynamics are nonlinear, stochastic, and involve multiple scales of resolution both in time and space.
- Establish a design-oriented theoretical framework that explicitly accounts for interactions among circuits, between the circuits and the cell machinery, and provides engineering solutions to mitigate the undesirable effects of these interactions;
- Develop design-oriented analysis tools to quantify the propagation of stochasticity through the nonlinear dynamics of biological networks;
- Develop a quantitative methodology to incorporate spatial heterogeneity effects into the analysis and design framework;
- Develop prototype experimental systems and a concrete demonstration of the integration of sensors that robustly distinguish between different chemical signatures.
None to date.
- Agency: Air Force Office of Scientific Research
- Grant number: FA9550-14-1-0060
- Start date: 1 Oct 2013
- End date: 30 Sep 2018
- Support: ~2 graduate students + supplies
- Reporting: annual reports due in June