Cancer treatment requires development of diverse therapeutic strategies. To this end, engineered bacteria offer promise for efficiently delivering and expressing genes with therapeutic effects and for selective tumor targeting. To fully realize bacteria's therapeutic potential, we need to overcome technological hurdles at multiple levels. These include development of a toolbox of genetic elements that can be pieced together to carry out therapeutic functions and a better understanding of design principles that will enable precise control of bacterial dynamics in the complex micro-environments of solid tumors. To address these challenges, the objective of this application is to use bacterial """"""""quorum sensing"""""""" modules to coordinate bacterial behavior (life, death, spatial aggregation) in diverse settings. Quorum sensing is a mechanism by which bacteria sense and respond to changes in their population density. Built upon strong preliminary data, the proposed research will focus on design, modeling, implementation, and characterization of two synthetic bacterial multicellular systems in Escherichia coli. The first system (Aim 1. a predator-prey system) will attempt to program the interaction of two bacterial populations that mutually regulate their gene expression;the circuit logic and dynamics resemble well-studied predator-prey ecosystems.
The second (Aim 2. a targeted consensus circuit) will program bacteria to target tumor cells with high specificity through coordinated decision making by two communicating bacterial populations. The proposed research is innovative, because it extends basic concepts and design methods of synthetic biology to address the pressing issue of cancer therapy. Its outcome will have significant impact by setting a solid foundation for engineering bacteria with highly reliable behavior for therapeutic applications. In particular, expected outcomes include (1) a repertoire of well-characterized genetic elements, modules, and systems, (2) insights into fundamental design laws for robust control of cellular dynamics in complex environments such as solid tumors, and (3) thoroughly tested modeling tools and methods. All of these can be applied in systems beyond the proposed ones and will be shared with the biomedical research community. Relevance to Public Health: The proposed research will fill the important gap that limits the application of engineering design strategies to the development of cancer-targeting bacteria. This approach will offer the extremely high targeting selectivity and bacterial containment efficiency needed for effective and safe cancer therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA118486-05
Application #
7879518
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Couch, Jennifer A
Project Start
2006-08-16
Project End
2012-07-31
Budget Start
2010-08-01
Budget End
2012-07-31
Support Year
5
Fiscal Year
2010
Total Cost
$208,402
Indirect Cost
Name
Duke University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Pai, Anand; Srimani, Jaydeep K; Tanouchi, Yu et al. (2014) Generic metric to quantify quorum sensing activation dynamics. ACS Synth Biol 3:220-7
Riccione, Katherine A; Smith, Robert P; Lee, Anna J et al. (2012) A synthetic biology approach to understanding cellular information processing. ACS Synth Biol 1:389-402
Tanouchi, Yu; Smith, Robert P; You, Lingchong (2012) Engineering microbial systems to explore ecological and evolutionary dynamics. Curr Opin Biotechnol 23:791-7
Payne, Stephen; Smith, Robert Phillip; You, Lingchong (2012) Quantitative analysis of the spatiotemporal dynamics of a synthetic predator-prey ecosystem. Methods Mol Biol 813:315-30
Song, Hao; You, Lingchong (2012) Modeling spatiotemporal dynamics of bacterial populations. Methods Mol Biol 880:243-54
Hallen, Mark; Li, Bochong; Tanouchi, Yu et al. (2011) Computation of steady-state probability distributions in stochastic models of cellular networks. PLoS Comput Biol 7:e1002209
Song, Hao; Payne, Stephen; Tan, Cheemeng et al. (2011) Programming microbial population dynamics by engineered cell-cell communication. Biotechnol J 6:837-49
Brenner, Katie; Arnold, Frances H (2011) Self-organization, layered structure, and aggregation enhance persistence of a synthetic biofilm consortium. PLoS One 6:e16791
Tanouchi, Yu; Pai, Anand; You, Lingchong (2009) Decoding biological principles using gene circuits. Mol Biosyst 5:695-703
Dougherty, Michael J; Arnold, Frances H (2009) Directed evolution: new parts and optimized function. Curr Opin Biotechnol 20:486-91

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