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
Recall Huang Inger paper- intro to what Boolean Networks are

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
explain level of abstraction

5. Model System Asthagiri and Lauffenburger, 2001 Anabi et al., 2001
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
explain why we see oscillations, and that IR serves to just increase the amplitude

10. Fuzzified Model Go to Simulink: Introduction to fuzzy logic
Membership functions
Rule based logic
Show working model
mat/sim/fuz
playworkspace
boolmodelwithdelays
happymodel
IRS_fuz2
vimportantfigure (desktop)

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, (2001)
Assoian and Schwartz, Curr. Opin. Genet. Dev. 11, (2001)
Asthagiri and Lauffenburger, Biotechnol. Prog. 17, (2001)
Asthagiri, Reinhart, Horwitz, and Lauffenburger, J. Cell Sci., 113, (2000)
Asthagiri et.al., J. Biol. Chem., 274, (1999)
Eliceiri, Circ. Res., 89, (2001)
Giancotti and Ruoslahti, Science 285, (1999)
Guilherme , Torres, and Czech, J. Biol. Chem., 273, (1998)
Huang and Ferrell, PNAS, 93, (1996)
Huang and Ingber, Exper. Cell Res. 261, (2000)
Schwartz and Baron, Curr. Opin. Cell Biol. 11, (1999)
Vuori and Ruoslahti, Science 266, (1994)