Investigating biological pattern formation with computational and synthetic tools
Abstract/Contents
- Abstract
- Pattern and structure exist throughout the biological world. In addition to being aesthetically beautiful on the surface, biological patterning is almost universally coupled to some sort of underlying function or purpose. Understanding structure-function relationships requires the ability to perturb relevant variables. This thesis discusses the development of a pair of tools -- one mathematical, one synthetic -- designed to help us understand biological patterning within the context of multicellular systems. In such systems, groups of cells self-organize into distinctly nonrandom patterns, often with single-cell precision. The mechanisms that explain how, and the reasons that explain why this occurs are the focus of the two projects discussed in this thesis. The first project, a cooperative effort between myself and David Glass, is motivated by the question of patterning robustness in multicellular tissues that develop via the lateral inhibition motif. Such tissues emerge with a precise alternating checkerboard or stripe-like pattern based on the repression of neighbouring cells, but it remains difficult to explain how biological systems with significant sources of noise and signaling delay are able to generate such robust patterns within the time constraints imposed during tissue development. We addressed this problem by developing a novel mathematical model for lateral inhibition that explicitly accounts for signaling delays and noise, and explored how delays affect patterning fidelity. Surprisingly, we uncovered that biological delays in the proper signaling context are able to reduce patterning error-rates in developing tissues, and that this increase in patterning fidelity is coupled to longer differentiation times, suggesting that natural systems strike a balance between patterning speed and precision. The second project is motivated by the observation of multicellular patterning in bacterial biofilms and how it is coupled to underlying ecological interactions. While these insights have largely been gained through observational science, an improved understanding of this relationship between patterning and ecology could be derived from new engineering tools that allow us to construct synthetic biofilm communities de novo with control over relevant patterning variables such as shape and geometry. It is with this aim in mind that I set out to engineer bacteria that could be patterned into biofilm communities. I accomplished this by applying light-regulated gene expression tools in conjunction with knowledge from biofilm physiology to create strains of bacteria that form bacteria under optical illumination. I then used these strains in conjunction with structured optical illumination to pattern biofilm communities. I discuss the development/characterization of light-responsive biofilm strains, and ongoing/future work that uses these strains in the construction of biofilm communities, consortia-based metabolic engineering, living biomaterials, integrated biosensors/diagnostics, and beyond. Overall, the work in this thesis provides the basis for a build-to-understand framework of investigating multicellular patterning, with the goal of revealing deeper structure-function relationships in these systems. It represents a small step towards a future where, guided by mathematical models, we can predictably and reliably engineer synthetic multicellular systems from the bottom up.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Jin, Xiaofan |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Riedel-Kruse, Hans |
| Thesis advisor | Riedel-Kruse, Hans |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Endy, Andrew D |
| Thesis advisor | Spormann, Alfred M |
| Advisor | Covert, Markus |
| Advisor | Endy, Andrew D |
| Advisor | Spormann, Alfred M |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Xiaofan Jin. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Xiaofan Jin
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...