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Improving Boolean Networks to Model Signaling Pathways

This project evaluates the efficacy of Boolean-like networks in modeling cellular signaling events, addressing difficulties in acquiring quantitative signaling data. It aims to explore abstract modeling capabilities and make qualitative predictions about key nodes in signaling pathways. The study incorporates systems such as fibronectin and insulin signaling, implementing both Boolean and fuzzy logic networks. Results indicate that while Boolean networks fall short of capturing complex signaling behavior, fuzzy networks effectively represent interactions and crosstalk even in data-scarce scenarios. Future work will focus on insulin signaling pathway and virtual experimentation with crosstalk sensitivity.

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Improving Boolean Networks to Model Signaling Pathways

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  1. Improving Boolean Networks to Model Signaling Pathways Bree Aldridge Diana Chai BE.400 Term Project December 5, 2002

  2. Outline • Motivation / Project Goals • Introduction to Model System • Implementation: • Boolean network • Fuzzy network • Results / Conclusions • Future Work

  3. Motivation • Cellular states control behavior • Quantitative signaling and state data difficult to obtain • Boolean-like networks: • Representative of how signaling networks process and transmit information • “Simpler” than solving a huge system of ODEs • Tool to explore subnetwork interactions (crosstalk) • Missing data holes may be filled in with intuition

  4. Project Goals • Explore the use of Boolean-like networks to model signaling events • Determine level of abstraction to which Boolean-like networks are useful • Make qualitative predictions about important nodes in signaling pathways

  5. Model System Fibronectin a5b1 Insulin Grb2 Insulin Receptor FAK/Src IRS1 Sos P13K Ras Akt/PKB Raf Mek Erk DNA Synthesis Asthagiri and Lauffenburger, 2001 Anabi et al., 2001

  6. Transient Behavior Asthagiri and Lauffenburger, 2001

  7. DNA Synthesis Asthagiri et.al., 2000

  8. Pure Boolean Model

  9. Pure Boolean Output

  10. Fuzzified Model • Go to Simulink: • Introduction to fuzzy logic • Membership functions • Rule based logic • Show working model

  11. Results : Time course

  12. Results: DNA Synthesis

  13. Take-home Results • Fuzzy logic networks are capable of capturing qualitative features of signaling networks (e.g. crosstalk) • Easy to build despite lack of quantitative information • Good for testing hypotheses at higher level of abstraction than ODE-based models

  14. Crosstalk Example

  15. Conclusions • Boolean Networks are NOT sufficient to capture complex behaviors of signalling networks where behavior is not ALL or NONE • Fuzzy Logic Networks are best used at the qualitative prediction level • Also good for exploring how subnetworks interact • Especially good for when data is lacking

  16. Future Work • Explore the insulin signaling pathway • Explore different levels of crosstalk • Explore sensitivity by changing membership functions and weighting rules

  17. References • Annabi, Gautier, and Baron, Fed. Eur. Biochem. Soc.,507, 247-252 (2001) • Assoian and Schwartz, Curr. Opin. Genet. Dev.11, 48-53 (2001) • Asthagiri and Lauffenburger, Biotechnol. Prog. 17, 227-239 (2001) • Asthagiri, Reinhart, Horwitz, and Lauffenburger, J. Cell Sci.,113, 4499-4510 (2000) • Asthagiri et.al., J. Biol. Chem.,274, 27119-27127 (1999) • Eliceiri, Circ. Res., 89, 1104-1110 (2001) • Giancotti and Ruoslahti, Science285, 1028-1032 (1999) • Guilherme , Torres, and Czech, J. Biol. Chem.,273, 22899-22903 (1998) • Huang and Ferrell, PNAS, 93, 10078-10083 (1996) • Huang and Ingber, Exper. Cell Res.261, 91-103 (2000) • Schwartz and Baron, Curr. Opin. Cell Biol.11, 197-202 (1999) • Vuori and Ruoslahti, Science266, 1576-1578 (1994)

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