The design of artificial genetic networks constitutes a powerful tool to regulate cellular physiology. Simple regulatory structures comprised of a few interacting genes can be assembled to engineer desirable phenotypes and control the biosynthesis of end products of biomedical and/or biotechnological interest. This doctoral thesis has focused on the in silico design of artificial genetic networks to drive the biosynthesis of a specific product of biotechnological interest, namely, PHA copolymer chains with desirable structures. In order to understand this complex process, a mathematical model was developed to describe the coupling between the dynamics of polymer and monomer formation and those of the genetic networks. The modeling studies have focused on the utilization of two synthetic networks, known as the genetic toggle and repressilator. The results indicate that the bistable toggle allows regulating the monomer composition of PHA copolymers. The use of the repressilator offers a higher level of control, as it enables the synthesis of PHA block copolymers with different length and composition of each of the blocks that comprise the chains. Additional computational studies have revealed the possibility to achieve superior performance than that of the repressilator, through the design of a novel genetic network that exhibits oscillatory dynamics with minimal overlap amongst gene expression levels. The oscillations were also found to be robust to stochastic fluctuations. Finally, an existing mathematical model was modified to explain the discrepancy of the original repressilator model with experimental data. The modeling studies support the hypothesis that non-specific interactions may also be present in addition to the original three promoter-repressor interactions, which the repressilator was designed to include.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/22254 |
| Date | January 2008 |
| Creators | Iadevaia, Sergio |
| Contributors | Zygourakis, Kyriacos |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | 206 p., application/pdf |
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