| Summary: | 碩士 === 國立臺北科技大學 === 化學工程與生物科技系化學工程碩士班 === 107 === The intracellular noise is ubiquitous and indispensable for biological systems, strong noise may be essential to some gene regulatory mechanisms, but it may be harmful to the others. Therefore, the methods of adjusting the noise represent the ability to control the performance of the cell population, and it has a high application value. In this study, the stochastic simulation algorithm(SSA) was used to simulate the situation of a switching system between the activation state and inactivation state genes which are controlled by transcriptional factors(TFS). With this method, we can utilize TFS expression level to control the downstream protein expression level and coefficient of variation(COV) The simulation results showed that TFS has a specific relationship with downstream proteins COV, we found that increasing TFS expression level would lead to a decreasing COV of downstream proteins, and vice versa. We also found that the noise intensity of transcription factors exists a special correlation with the frequency of DNA switching between activated and inactivated states. Due to this special feature, DNA is able to block out the upstream noise. We verify the conclusion in different situations, of which DNA are at a high switching frequency state and at a low switching frequency state. The simulation results show that most of the noise yield from TFS can be effectively filtered whether the system is at a high switching frequency state or at a low switching frequency state. Last we use analytical solution calculated from probability generating function(PGF) to verify that the mean value of the TFS, rather than its noise intensity, actually dominates the distribution of the downstream protein in most cases. With those research results above, we can simply manipulate downstream protein noise intensity by adjusting the TFS mean expression level. The research of manipulating noise intensity at the downstream protein level has a high reference value in the field of synthetic biology and computational biology.
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