Synthetic Development Biology: From cells to materials
Abstract
The proposed research effort aims to engineer synthetic multi-cellular patterns that can form the basis of a new class of biological materials. Specifically, a synthetic biology approach will be taken to integrate engineered gene regulatory components with natural and synthetic cell-cell communication modules in yeast strains (a unicellular organism) to create yeast strains capable of supporting activator-inhibitor (Turing) patterns. Activator-inhibitor mechanisms were first proposed by Alan Turing in 1952 to play a role in the ability of a population of cells to self-organize into a spatially differentiated pattern with long-range order. There are many biological patterns, from animal coats to digit patterning during development, which are highly suggestive of an underlying Turing mechanism. However, the identification and characterization of the molecules acting as activators and inhibitors responsible for the patterning have proven difficult. In the proposed research, yeast hormone alpha factor will first be used as the activating signal and the plant hormone Auxin will first be used as the inhibiting signal, but a variety of other signaling molecules will also be explored in the research effort. The Co-Principal Investigator on this proposal has recently demonstrated that Auxin can be biosynthesized by a synthetic strain of yeast and can result in a gene regulatory response when sensed by a receiving strain of yeast. Additionally, the Principal Investigator on this proposal has demonstrated that yeast mitogen-activated protein kinases (MAPK) signaling can be rewired to target any protein of interest for degradation in response to alpha-factor signaling. Thus, Auxin and alpha factor have widely different cellular synthesis and response pathways, and the orthogonality of the cellular response makes them good candidates for engineering activator inhibitor patterns. The research plan consists of three aims. In the first aim, yeast strains will be engineered that produce and respond to alpha-factor, Auxin, and a variety of other signals. Additionally, control over the input-output relationship that connects signal concentrations to cellular response will be demonstrated. Finally, approaches for controlling both signal half-life and diffusion rates will be developed and implemented. In the second aim, the modules developed under the first aim will be combined into a set of dynamical systems that produce patterns in response to a spatial gradient of one or multiple input signals. Bistable systems that convert a graded signal into patterns with sharp boundaries will be created as will feed-forward loops that result in stripes of gene expression, and combinatorial logic for pattern refinement. In the third aim, a set of activator inhibitor networks from the modules and systems developed under aims 1 and 2 will be assembled. The system~s parameter space will systematically be explored, and both oscillations and Turing pattern formation will be demonstrated. The approach taken in the proposed research effort is the first step towards rationally designed self-organizing biological materials. Yeast such as S. cerevisiae and C. albicans form complex biofilms with a range of interesting materials properties. Moreover, yeast is used as the host for the biosynthesis of a wealth of substances with interesting chemical, mechanical, optical, or even electronic properties. It is easy to imagine that a Turing pattern mechanism could control the organization of a biofilm or could control the distribution of materials within a biofilm. Such patterning could occur without prior spatial information, and patterns could regenerate autonomously when they are disturbed, thus moving closer to the goal of creating self-organizing and adaptive materials. It is impossible to predict the impact of a materials~ manufacturing technology based on self-organization, but a programmable synthetic biological manufacturing technology could yield
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 23, 2016
- Source ID
- N000141613189
Entities
People
- Georg Seelig
Organizations
- Office of Naval Research
- United States Navy
- University of Washington