Methods for Predicting Synthetic Gene Circuits
| dc.contributor.advisor | Bennett, Matthew | |
| dc.contributor.committeeMember | Ott, William | |
| dc.creator | Zong, David Mao | |
| dc.date.accessioned | 2020-02-25T21:22:57Z | |
| dc.date.available | 2020-02-25T21:22:57Z | |
| dc.date.created | 2020-05 | |
| dc.date.issued | 2020-02-25 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-02-25T21:22:57Z | |
| dc.description.abstract | Mature engineering disciplines use computational tools to test designs before they are built, which allows rapid engineering design-build-test cycles. Synthetic biology is an immature engineering discipline because there is a dearth of computational tools that accurately predict how engineered systems behave. A strategy to improve computational methods is to build well-defined toy systems and study them using computational modeling. In this thesis, I built two such toy systems and studied their behavior using computational modeling. The first gene circuit I designed transcribes a gene of interest in response to multiple chemical signals. This design uses modular transcription factors to increase the number of possible chemical input combinations. However, this design is difficult to fully characterize because the number of possible input concentrations and input combinations are too numerous. We constructed a predictive model that accurately predicts gene expression for a set of input chemicals at any input concentration. The next system that I built is composed of three strains of engineered E. coli that interact using cell-cell signaling. This engineered population of bacteria pulses gene expression response to external signal. This pulse was modulated by changing the population fraction of each member species. We developed a computational model that predicts the behavior of the population in response to cell strain ratio. My work shows that complex synthetic biological systems can be tuned rationally and predictably using computational tools which makes engineering biology quicker. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Zong, David Mao. "Methods for Predicting Synthetic Gene Circuits." (2020) Diss., Rice University. <a href="https://hdl.handle.net/1911/108076">https://hdl.handle.net/1911/108076</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/108076 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Synthetic Biology | |
| dc.subject | Prediction | |
| dc.title | Methods for Predicting Synthetic Gene Circuits | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Systems, Synthetic and Physical Biology | |
| thesis.degree.discipline | Natural Sciences | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.major | Synthetic Biology | |
| thesis.degree.name | Doctor of Philosophy |
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