An RNA-based framework for cellular dynamic controls - towards building a genetic operational amplifier
Abstract/Contents
- Abstract
- Biological systems maintain functioning in dynamic environments by performing complex computations to determine proper responses. These "computation" processes can be decomposed and implemented by fundamental genetic controllers, such as amplifiers and differential sensors. Despite a growing number of examples of synthetic control systems, limited effort has been directed toward building a generalizable controller for such dynamic regulations. We proposed to design a genetic operational amplifier (gen-opamp), which can serve as a fundamental component for electronic circuit systems. This gen-opamp consists of three stages: a differential sensor, an amplifier, and an output stage. The differential sensor contains a molecular transducer that maps the inputs to mRNA levels, and an RNA antisense subtractor to perform differential calculation. The genetic differential signal is further amplified by a transcriptional device, which subsequently regulates the expression of an enzymatic output to form feedback loops. The non-feedback configuration of the gen-opamp functions simply as a comparator; whereas its feedback configuration acts as a level-setter that keeps the ratio of the inputs constant by actively adjusting one to the other. We designed a prototype gen-opamp in budding yeast, using theophylline and tetracycline as the inputs. The corresponding aptamer-coupled ribozymes (i.e., RNA switches) are used as molecular transducers to regulate mRNAs at 3' UTR, together with a mutual complementary sense-antisense pair (at 5' UTR) to achieve differential sensing. partially implemented this Two amplifiers are implemented using synthetic LexA- and TetR-based transcriptional devices, driving either an activator (caffeine demethylase) or deactivator (theophylline demethylase) to form enzymatic feedback loops targeting the theophylline level. We began by engineering and verifying biological parts in each stage of the gen-opamp, and further developed a model-facilitated approach to facilitate the assembly process of each stage (i.e., level-matching). We also built a multi-chamber chemostat that enables continuous growth and sampling for characterizing the dynamic regulations under closed-loop controls. We were able to partially assemble the gen-opamp (as an input-controlled amplifier) and achieved a gain on the order of 200. We further demonstrated how to this partially assembled amplifier to form closed-loop feedback, and were able to observe hysteresis effect on theophylline production (positive feedback) and relaxation effect (negative feedback). This thesis presented a strong groundwork for the full assembly of the designed gen-opamp, which is currently still in the process. Future work involves putting both feedback loops into a single system together with the RNA antisense to implement the gen-opamp.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wang, Yen-Hsiang |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Smolke, Christina D |
| Thesis advisor | Smolke, Christina D |
| Thesis advisor | Das, Rhiju |
| Thesis advisor | Riedel-Kruse, Hans |
| Thesis advisor | Wooley, Bruce A, 1943- |
| Advisor | Das, Rhiju |
| Advisor | Riedel-Kruse, Hans |
| Advisor | Wooley, Bruce A, 1943- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yen-Hsiang Wang. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Yen-hsiang Wang
- 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...