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The Frequency Dependence of Osmo-Adaptation in Saccharomyces cerevisae

This paper presentation explores yeast's response to osmotic shock using systems-engineering methods to develop a predictive model of signaling dynamics. Discoveries include the role of MAPK in osmotic regulation, shedding light on cellular models and new approaches. Techniques such as Fourier analysis and model extraction reveal insights into osmotic stress response at varying frequencies. The study also discusses the impact of gene expression on short and long-term adaptation in yeast, emphasizing the importance of protein synthesis in cellular response.

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The Frequency Dependence of Osmo-Adaptation in Saccharomyces cerevisae

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  1. Phillip Samayoa 20.309 – Paper Presentation October 9, 2008 The Frequency Dependence of Osmo-Adaptation in Saccharomyces cerevisae Jerome T. Mettetal, Dale Muzzey, Carlos Gómez-Uribe, Alexander van Oudenaarden

  2. Introduction • Objective • Determine the dominant processes of yeast’s response to osmotic shock • Approach • Systems-engineering methods • Back out a predictive model of signaling dynamics • Significance • Improved understanding of MAPK’s role in osmotic regulation • New approach to developing cellular models

  3. High Osmolarity Response • Less glycerol export (FPS protein) • HOG1 MAPK is transported to the nucleus • indication of osmotic stress • transcription • Measurement of Osmotic Stress • HOG1 tagged with YFP • Nuclear Protein tagged with RFP • (<YFP>nuclear/<YFP>cell)population

  4. Input Stimulus & Measure Output

  5. Fourier Analysis Extracts a Predictive Model • \ Example (RC circuit): Input = Vo*sin(wt) Vout = sin(wt)*Vo(1 + iwRC)-1

  6. The Backed out Model can Predict the Cell’s Step Response

  7. The LTI Model can be Converted into a Molecularly defined Model

  8. Mutant Strain Showed Delayed Short-Term Response Dynamics

  9. Gene Expression Mediates Response Over Longer Time Scales Cells can synthesize proteins Cells can’t synethsize proteins

  10. Summary • Engineering principles to predict response of a system • Moving Forward • Measure state-space variables

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