Using engineered gene circuits as a model system, the proposed work aims to examine the scaling properties of self-organized patterns. It focuses on a unique property, scale invariance, or maintenance of constant relative size of an organ with respect to the whole body during animal development or between individuals. The project represents a transformative application of synthetic biology to address unresolved, fundamental questions in biology. The proposed computational and experimental framework provides a well-defined context for exploring design principles that underlie generation of self-organized pattern formation as well as the emergent scaling properties. In addition to offering new biological insights, the proposed engineered systems can serve as the foundation for future engineering endeavors, such as fabrication of novel biomaterials. Furthermore, experimental techniques and computational infrastructure arising from the proposed work will be applicable for analyzing both natural and synthetic biological networks. Both computational and experimental systems and tools will be made available to the broad research community.
Equally important, the research in this project will provide opportunities for pre-college students, undergraduates, and graduate students with backgrounds in biology, engineering, mathematics, and physical sciences to become exposed to interdisciplinary research in bioengineering and synthetic biology. Modeling examples and experiments derived from the proposed research, along with other examples drawn from the literature, will be used to train these students. In addition, the course and curriculum development will facilitate dissemination of knowledge in Systems and Synthetic Biology both at and beyond Duke University. Finally, to support both short-term and long-term education goals, the proposed efforts include development of a textbook on systems and synthetic biology that targets upper-level undergraduate students and starting graduate students.
aims to use a combination of mathematical modeling and experimental analysis of synthetic gene circuits to explore the fundamental mechanisms underlying scaling properties of self-organized patterns. Scale invariance refers to maintenance of constant relative size of an organ with respect to the whole body during animal development or between individuals. A number of mechanisms have been proposed to explain scale invariance in biological pattern formation. Regardless of their specific molecular interactions, however, the vast majority of these mechanisms require morphogen gradients as the spatial cue, which are either predefined or generated as part of the patterning process. In preliminary work, using Escherichia coli programmed by a synthetic gene circuit, the investigator has demonstrated the generation of perfect scale invariance of ring size versus colony size in robust, self-organized ring patterns of gene expression in the absence of an apparent morphogen gradient. This observation raises a fundamental, unresolved question: How does scale invariance occur in self-organized patterns in the absence of a spatial morphogen gradient? To address this question, the investigator proposes to develop and optimize an experimental platform to examine scaling properties of self-organized pattern formation in engineered bacteria. This platform couples inkjet printing technology and synthetic gene circuitry to explore the role of morphogen as a temporal cue in the pattern formation process, guided by mechanistically based mathematical models.
This award is funded jointly by the Systems and Synthetic Biology Program in MCB and the Biotechnology, Biochemical and Biomass Engineering Program in CBET.
