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Stochastic Analysis of Bi-stability in Mixed Feedback Loops

Stochastic Analysis of Bi-stability in Mixed Feedback Loops. Yishai Shimoni, Hebrew University CCS Open Day Sep 18 th 2008. An Integrated Network. A feedback loop consists of two genes that regulate each other’s expression

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Stochastic Analysis of Bi-stability in Mixed Feedback Loops

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  1. Stochastic Analysis of Bi-stability in Mixed Feedback Loops Yishai Shimoni, Hebrew University CCS Open Day Sep 18th 2008

  2. An Integrated Network • A feedback loop consists of two genes that regulate each other’s expression • In a Mixed Feedback Loop (MFL) each gene uses a different mechanism for the regulation

  3. Small RNAs (sRNAs)‏ • Non-coding RNA molecules • 50-400 nucleotides long • Base-pairs with mRNAs and influences translation (normally repression) • Approximately 100 sRNAs identified in E. coli • Participate mostly in stress responses due to fast synthesis

  4. Double Negative Mixed Feedback Loop (MFL) A s B A Time (sec x 105)‏ Time (sec x 104)‏ Bi-stability Meta-stability

  5. Double Negative MFL

  6. Double Negative MFL

  7. Double Negative MFL • Questions: • How much of the parameter range displays a meta-stable state? • Does this happen with protein-protein interactions as well? • What is the difference? • Run the Monte Carlo simulation with different parameters and check if the state (A dominated or s/B dominated) changes during a given time

  8. Double Negative MFL Phase Map of bi-stability in sRNA double negative MFL

  9. Double Negative MFL Phase Map of bi-stability in protein-protein double negative MFL

  10. Double Negative MFL • Conclusion: • Stochastic analysis reveals a new dynamic behavior • Cannot be seen using deterministic analysis • Quantitative difference between MFLs with sRNA regulation and MFLs with protein-protein interaction • Both have same qualitative dynamics • Do simulations fit reality?

  11. In the presence of iron Fur represses RyhB transcription Iron depletion: Fur does not repress RyhB RyhB highly expressed RyhB Regulates many iron uptake genes Fur-RyhB MFL in E. Coli

  12. Fur-RyhB MFL in E. Coli • Bi-stability is unsuitable RyhB Fur Time (sec x 10-5)‏ • A meta-stable state is perfect! RyhB Fur Time (sec x 10-4)‏

  13. Summary • Post transcriptional regulation by sRNA • Offers different dynamics than other kinds of regulation • The dynamics are utilized by the cell • Mathematical Models using stochastic analysis can capture important features of the dynamics of biological networks

  14. Acknowledgements • Modeling: • Prof. Ofer Biham • Adiel Loinger • Guy Hetzroni • Networks integration, circuit identification: • Prof. Hanah Margalit • Dr. Gilgi Friedlander • Gali Niv • Parameters and sRNA: • Prof. Shoshy Altuvia Y. Shimoni et. Al, submitted to PLoS Comp Biol

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