Stochastic Modeling of Transcription Factor Binding Fluctuations

stochastic;transcription

Pirone, Jason R ; Timothy C. Elston, Committee Co-Chair,Alun L. Lloyd, Committee Member,Charles E. Smith, Committee Co-Chair,Robert C. Smart, Committee Co-Chair,Jason M. Haugh, Committee Member,Pirone, Jason R ; Timothy C. Elston ; Committee Co-Chair ; Alun L. Lloyd ; Committee Member ; Charles E. Smith ; Committee Co-Chair ; Robert C. Smart ; Committee Co-Chair ; Jason M. Haugh ; Committee Member

University:North Carolina State University

关键词: stochastic;    transcription;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/3935/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Cell populations typically exhibit binary or graded transcriptional responses to external stimuli.Transcription factor interactions with DNA have been hypothesized to account for both of these scenarios.To address this hypothesis, two stochastic models were constructed to describe transcription in simple, engineered eukaryotic systems.In the first system, three transcription factors bind independently to enhancer sites directing production of protein.This system has no regulation in the form of feedback loops, but the system nonetheless exhibits a clear binary response when transcription factor binding fluctuations are slow.The graded response occurs when transcription factor binding fluctuations are rapid.Thus, transcription factor binding fluctuation is an important mechanism underlying and reconciling the graded and binary transcriptional responses.In the second model, the influence of autoregulatory feedback loops on transcription was assessed.Autoregulated systems are capable of exhibiting bistability, a mechanism cited to explain the binary transcriptional response.In this autoregulated system, a dimeric protein acts as a transcription factor to increase its own production.Using biologically realistic parameter values, the system was determined not to be bistable.However, binary transcriptional responses were still observed in stochastic models due to discrete fluctuations in transcription factor binding.The results of both models suggest that transcription factor binding fluctuations play an important, and often overlooked role, in observed patterns of transcriptional activation.