Predictable Engineering of Self-organized Spatial Patterns in Bacteria
Abstract
The ability to program complex self-organized patterns in living cells has profound implications for both basic understanding of pattern formation in nature and for diverse future applications. These applications include living fabrication of functional materials, information encoding and decoding, and distributed computation. Despite nearly two decades of efforts in synthetic biology, however, the progress in programming self-organized patterns has been limited, in comparison with other types of dynamics. For the few successful examples, the programmed patterns are relatively simple, in contrast to the incredibly diverse and robust patterns in nature. The proposed research aims to overcome this gap by developing a mechanistic understanding of the ~chassis~ to generate sophisticated patterns, by focusing on the branching patterns generated by Pseudomonas aeruginosa. Building on this understanding, the proposed research will develop a platform that will enable rapid and predictable programming of spatial patterns using synthetic gene circuits and by integrating mechanistic modeling, machine learning, and high-throughput experimentation. The proposed research has several significant, broad implications. First, the engineered patterns, when sophisticated enough, can serve as the foundation for information encoding and decoding. Second, the proposed research will provide mechanistic insights into natural patterning processes in bacteria and other organisms. Third, the pipeline underlying the proposed research will be applicable for engineering other types of spatial patterns of interests. APPROVED FOR PUBLIC RELEASE.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 11, 2020
- Source ID
- N000142012121
Entities
People
- Lingchong You
Organizations
- Duke University
- Office of Naval Research
- United States Navy