| Summary: | 碩士 === 國立臺灣大學 === 分子與細胞生物學研究所 === 102 === Compared to an unregulated gene, auto-regulation or positive-feedback loop (PFL) in a transcriptional network is known to have the characteris&;#172;tic of noise amplification, leading to larger expression variations for cells with identical genetics. A high cell-cell variation potentially offers greater chance of survival for the population in a changing environment. PFL, however, also increases mean expression level, making it difficult to conclude the survival advantage comes from greater variation. To over&;#172;come this, we aim to normalize the expression level of PFL to that of an unregulated gene. We chose AraC expression in E coli as our model system, and designed a series of AraC and RNA polymerase bind&;#172;ing sites in AraC promoter to provide a spectrum of binding affinity and thus expression/feedback strengths. RFP was used as to report expres&;#172;sion level. We also used Gillespie algorithm to predict which combina&;#172;tions of -35 and/or -10 regions (according to the Anderson library) and AraC cooperativity/affinity that would most likely to generate same pro&;#172;tein expression for unregulated gene and PFL. With these predictions, we constructed plasmids, transformed them into E coli, and analyzed RFP strength by flow cytometry, and will further confirm if these data agrees with our computational predictions. Our ultimate goal is to replace RFP with an antibiotic gene (e.g. ampicillin), and test if a larger expression of this resistance gene provides greater survival advantages with a fluctuat&;#172;ing/higher ampicillin concentrations.
|
|---|
