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Applying Bayesian networks to modeling of cell signaling pathways

Applying Bayesian networks to modeling of cell signaling pathways. Kathryn Armstrong and Reshma Shetty. Outline. Biological model system (MAPK) Overview of Bayesian networks Design and development Verification Correlation with experimental data Issues Future work. MAPK Pathway. E2. E1.

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Applying Bayesian networks to modeling of cell signaling pathways

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  1. Applying Bayesian networks to modeling of cell signaling pathways Kathryn Armstrong and Reshma Shetty

  2. Outline • Biological model system (MAPK) • Overview of Bayesian networks • Design and development • Verification • Correlation with experimental data • Issues • Future work

  3. MAPK Pathway E2 E1 KKK KKK* KK KK-P KK-PP KK’ase K K-P K-PP K’ase

  4. Overview of Bayesian Networks Givens: Burglary Earthquake Alarm

  5. E2 E1 KKK KKK* KK KK-P KK-PP KK’ase K K-P K-PP K’ase Bayesian network model

  6. Simplifying Assumptions • Normalized concentrations of all species • Discretized continuous concentration curves at 20 states • Considered steady-state behavior

  7. The key factor in determining the performance of a Bayesian network is the data used to train the network. Probability tables Training data Bayesian network

  8. Network training I: Data source • Current experimental data sets were not sufficient to provide enough information • Relied on ODE model to generate training set (Huang et al.) • Captured the essential steady-state behavior of the MAPK signaling pathway

  9. Network training II: Poor data variation

  10. 1D x 4 E1 E2 MAPKPase MAPKKPase Network training III: incomplete versus complete data sets 4D Time = (# samples) x 4 Time = (# samples)4

  11. Verification: P(Kinase | E1, P’ases) Bayesian network Huang et al.

  12. Verification: P(E1 | MAPK-PP, P’ases)

  13. Correlation with experimental data C.F. Huang and J.E. Ferrell, Proc. Natl. Acad. Sci. USA93, 10078 (1996).

  14. Correlation with experimental data J.E. Ferrell and E.M. Machleder, Science 280, 895 (1998).

  15. Where does our Bayesian network fail?

  16. Where does our Bayesian network fail?

  17. E2 E1 KKK KKK* KK KK-P KK-PP KK’ase K K-P K-PP K’ase Inference from incomplete data

  18. Future work • Time incorporation to represent signaling dynamics • Continuous or more finely discretized sampling and modeling of node values • Priors • Bayesian posterior • Structure learning

  19. Open areas of research • Should steady state behavior be modeled with a directed acyclic graph? • Cyclic networks Theoretically impossible Need an alternate way to represent feedback loops Hard, but doable

  20. Why use a Bayesian network? • ODE’s require detailed kinetic and mechanistic information on the pathway. • Bayesian networks can model pathways well when large amounts of data are available regardless of how well the pathway is understood.

  21. Acknowledgments • Kevin Murphy • Doug Lauffenburger • Paul Matsudaira • Ali Khademhosseini • BE400 students

  22. References • http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html • http://www.ai.mit.edu/~murphyk/Software/BNT/usage.html • A.R. Asthagiri and D.A. Lauffenburger, Biotechnol. Prog.17, 227 (2001). • A.R. Asthagiri, C.M. Nelson, A.F. Horowitz and D.A. Lauffenburger, J. Biol. Chem. 274, 27119 (1999). • J.E. Ferrell and R.R. Bhatt, J. Biol. Chem.272, 19008 (1997). • J.E. Ferrell and E.M. Machleder, Science280, 895 (1998). C.F. Huang and J.E. Ferrell, Proc. Natl. Acad. Sci. USA93, 10078 (1996). F. V. Jensen. Bayesian Networks and Decision Graphs. Springer: New York, 2001. • K.A. Gallo and G.L. Johnson, Nat. Rev. Mol. Cell Biol.3, 663 (2002). K.P. Murphy, Computing Science and Statistics. (2001). • S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall: New York, 1995. • K Sachs, D. Gifford, T. Jaakkola, P. Sorger and D.A. Lauffenburger, Science STKE148, 38 (2002).

  23. Network training IV: final data set

  24. Network training V: Final concentration ranges

  25. E2 E1 MAPKKPase Network training III: Observation of all input combinations 1D Visualization 3D Visualization E1 E2 MAPKPase MAPKKPase 4D Visualization Time = (# samples)4 2D Visualization

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