Browsing by Author "Timothy C. Elston, Committee Co-Chair"

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    Stochastic Modeling of the Behavior of Dynein
    (2005-04-11) Goedecke, David Michael; John F. Monahan, Committee Member; Kevin Gross, Committee Member; Carla Mattos, Committee Member; Sharon R. Lubkin, Committee Co-Chair; Timothy C. Elston, Committee Co-Chair
    Molecular motors are proteins that convert stored energy into physical work inside cells, and thus are the engines that drive many cellular functions. An individual motor can be studied using a laser trap to measure its response to working against an external force. Axonemal dynein is the molecular motor responsible for the rhythmic beating of eukaryotic cilia and flagella. An individual axonemal dynein molecule is capable of both unidirectional, processive motion and bidirectional motion when placed under a load (Shingyoji et al., 1998). This capability may be an important underlying factor in the mechanism for flagellar and ciliary motion. A detailed stochastic model is proposed which links the physical motion of a two-headed dynein molecule to the biochemical steps of its ATP hydrolysis cycle. Forward motion is driven by ATP hydrolysis, while backward motion is due to a passive process of biased diffusion. The model exhibits both processive and bidirectional behaviors. A simplified model which can be more easily analyzed is derived, as is an alternate version which steps backward actively, rather than sliding passively. The simplified models are then used to predict motor characteristics such as the load-velocity profile, the stall force, and the effective diffusion coefficient, which can be determined experimentally and used to distinguish among competing mechanisms.
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    Stochastic Modeling of Transcription Factor Binding Fluctuations
    (2004-08-22) 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
    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.