Robust Multi-Layer Control Systems for Cooperative Cellular Behaviors
The goal of this project is to develop and demonstrate a multi-layer intra- and inter-cellular control systems integrated to create complex, spatially-organized, multi-functional model system for wound healing. Our system makes use of a layered control architecture with feedback at the DNA, RNA, protein, cellular and population levels to provide programmed phenotypic differentiation and interconnection between multiple cell types.
This project is an active collaboration with John Doyle, Michael Elowitz and Niles Pierce. This page describes the activities taking place in Richard Murray's group.
Phase I objectives (Murray group):
- Biological controllers: Design and implement feedback controllers in E. coli to modulate growth rate in response to an input. Inputs will consist of small molecule inducers available in environment or secreted by other cells, and outputs will be bacterial concentrations.
- Testbeds: Develop microfluidic devices for temporal measurement and control of growth environments for bacterial systems.
- Testbeds: Develop spatially patterned testbeds for temporal measurement and control of bacterial cell population patterning.
- Theory: Design and simulate classes of controllers and identify those that fulfill performance objectives. Convert performance objectives into optimization constraints. Verify design robustness to perturbations in biological parts and in external environment.
- Theory: Develop predictive models for cooperative, multi-cellular systems for preliminary analysis and design of local input/output dynamics and interconnection structure.
- Cell-free and in vivo characterization of Lux, Las, and Rpa quorum activation systems in E. coli (Andrew Halleran, Richard M. Murray, Submitted, ACS Synthetic Biology, July 2017)
- Population regulation in microbial consortia using dual feedback control (Xinying Ren, Ania-Ariadna Baetica, Anandh Swaminathan, Richard M. Murray, Submitted, 2017 Conference on Decision and Control (CDC))
- Quantitative Modeling of Integrase Dynamics Using a Novel Python Toolbox for Parameter Inference in Synthetic Biology (Anandh Swaminathan, Victoria Hsiao, Richard M Murray, 2017 Synthetic Biology: Engineering, Evolution, and Design (SEED) Conference)
The project or effort depicted was or is sponsored by the Defense Advanced Research Projects Agency (Agreement HR0011-17-2-0008). The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.