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Specification and simulation of synthetic multicelled behaviors
Seunghee S Jang1, Kevin T Oishi, Robert G Egbert
1Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
ACS Synthetic Biology
|May 9, 2013
Summary
This study introduces a new framework and specification language, gro, for designing synthetic multicelled systems. It enables exploration of complex behaviors in microbial communities, like edge detection and morphogenesis, by simulating growth and signaling.
Area of Science:
- Synthetic biology
- Computational biology
- Microbial systems engineering
Background:
- Synthetic biology aims to engineer complex behaviors in microorganisms like E. coli and S. cerevisiae.
- Current design frameworks lack considerations for microcolony morphology, molecular signaling diffusion, and parallel algorithm implementation.
- Developing multicellular systems requires accounting for spatial dynamics and emergent behaviors.
Purpose of the Study:
- To introduce a novel framework for specifying and simulating synthetic multicellular behaviors.
- To bridge the gap between high-level algorithmic descriptions and low-level bimolecular implementations.
- To enable the design of sophisticated distributed algorithms in microbial consortia.
Main Methods:
- Development of a new specification language, gro, for multicelled systems.
- Integration of microcolony growth and molecular signaling simulations.
- Exploration of collective behaviors through high-level behavioral descriptions.
Main Results:
- Demonstration of the framework with previously published synthetic biology systems.
- Introduction of novel specifications for microcolony edge detection and programmed morphogenesis.
- Illustration of specification refinement for detailed bimolecular implementation.
Conclusions:
- The gro framework facilitates the design and simulation of complex multicellular behaviors.
- This approach allows for the exploration of emergent properties in synthetic microbial systems.
- The framework supports the translation of algorithmic concepts into biologically implementable designs.
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