© 2017 Living Computing Project.

Sponsored by National Science Foundation’s Expeditions in Computing Program

(Awards #1522074 / 1521925 / 1521759).

The behavior of synthetic cellular systems may exhibit complex, dynamic behavior and cannot be treated as digital in general. Additionally, cellular context causes nodes within a network to become unintentionally coupled, which deteriorates the function of the system. In this project, we measure, model, and analyze dynamic behavior and effects due to cellular context in order to increase the predictability of design-oriented models, optimize the design of genetic networks for robustness to context, and create design paradigms for genetic networks that scale well.

WHAT?

Effects due to cellular context hamper the predictability of current models and substantially deteriorates the  performance of genetic circuits. Models that capture context-effects are necessary to ensure agreement between predictions and experimental results and to inform decisions about the design and optimization of robust genetic networks.

WHY?

RESEARCH - ANALOG

Shared resource pools cause coupling that changes the behavior of a genetic network. We create design-oriented predictive models that allow to incorporate these context effects within the design process.

Retroactivity causes a deterioration of performance due to bidirectional signals when systems are connected. Using motifs inspired by natural systems, devices may be created to insulate systems from retroactivity while maintaining signal transmission.

Project Contributors

  • ​D. Del Vecchio, “Modularity, context-dependence, and insulation in engineered biological circuits”, Trends in Biotechnology, 2014.
     

  • A. Gyorgy, J. Jimnez, J. Yazbek, H.H. Huang, H. Chung, R. Weiss, D. Del Vecchio, “Isocost lines describe the cellular economy of genetic circuits” Biophysical Journal, August 2015.
     

  • D. Mishra, P. Rivera, A. Lin, D. Del Vecchio, R. Weiss, “A load driver device for engineering modularity in biological networks”, Nature Biotechnology, September 2014.

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