Theory-Based Engineering of Biomolecular Circuits in Living Cells

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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.

Current participants:
  • Vipul Singhal (PhD student, CNS)

Additional participants:

  • Ania Baetica (PhD student, CDS)
  • Xinying (Cindy) Ren (PhD student, CDS)
  • Anandh Swaminathan (PhD student, CDS)

Collaborators:

  • Domitilla Del Vecchio (MIT)
  • Jim Collins (BU)
  • Eduardo Sontag (Rutgers)

Past participants:

  • Enoch Yeung (PhD student, CDS)
  • Yutaka Hori (postdoc, CMS)

Overview

Afosr13-synbio.png

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.

Project objectives:

  • 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.

Publications




Research supported by the Air Force Office of Scientific Research, grant number FA9550-14-1-0060.

  • 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