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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01fn107139x
 Title: Information Transduction Capacity of the RhoA-MRTFA Signaling Axis Authors: Spar, Benjamin Advisors: Nelson, Celeste Department: Computer Science Class Year: 2016 Abstract: During tumor metastasis, RhoA signals through myocardin-related transcription factor-A (MRTF-A) to induce epithelial cells to adopt a mesenchymal phenotype in a process known as epithelial-mesenchymal transition. Here we present a quantitatively rigorous model based on stochastic chemical kinetics to accounts for both temporal dynamics and stochasticity of this process. This model was used to simulate the response of the RhoA-MRTFA signaling axis in response to serum stimulation. An adaptive timestepping algorithm was implemented to guarantee a limit on the numerical error of the simulations. These simulations were used to estimate the theoretical channel capacity of the signaling axis, from serum stimulation to MRTF-A activation. The actual (experimental) channel capacity was also measured in live cells. We found that the experimentally-measured channel capacity of the RhoA-MRTFA signaling axis is far less than the theoretical maximum, indicating that noise in the pathway signi cantly limits the sensitivity of the cell to extracellular cues. Interestingly, MRTF-A transcriptional activity is highly responsive to serum concentration, even though MRTF-A localization is not. Furthermore, measuring RhoA dynamics over time revealed that RhoA activation is highly stochastic, even in unstimulated cells, which is not explained by intrinsic noise in its regulatory mechanisms. Taken together, these data indicate that the RhoA-MRTFA regulatory axis alone is weakly sensitive to extracellular serum, but there exist other mechanisms to compensate for this lack of delity. Extent: 46 pages URI: http://arks.princeton.edu/ark:/88435/dsp01fn107139x Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Computer Science, 1988-2016

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