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1. Introduction
2. Talk outline
3. The evolution of systems biology
4. Dissecting molecular interaction networks
5. ARACNe
6. Reverse engineering regulatory networks
7. Computing the mutual information
8. How ARACNe works
9. Application to the MYC proto-oncogene
10. Can ARACNe be applied to signaling networks?
11. Experimental data for ARACNe
12. Dataset composition
13. Discrete data handling: ARACNe modification
14. ARACNe: EGFR network
15. MINDy- Modulator inference by network dynamics
16. Reverse engineering conditional interactions
17. Reverse engineering conditional TF interactions
18. Multivariate dependency model
19. A transistor-like 3-way interaction model
20. MINDy: conditional interactions
21. Silencing of STK38 affects c-Myc turn-over
22. STK38 affects MYC localization and activity
23. The effects of BHLHB2 on TERT transcription
24. Top MYC modulators identified by MINDy
25. Signalome transfactome network
26. Biochemical validation
27. BEIA: Bayesian evidence integration algorithm
28. Interactome validation
29. IDEA
30. IDEA workflow
31. Interaction dysregulation analysis
32. The "barcode" of cancer
33. IDEA scoring
34. What can IDEA identify?
35. Dysregulated genes identify dysregulated pathway
36. Master regulators of human phenotypes
37. The germinal center
38. The idea behind MRA
39. Differential activity of TFs
40. Shadow regulation and synergy
41. Statistical tests for shadowing and synergy
42. Shadow removal
43. 37 GC-activated pairs (16 TF)
44. Top activated MRs
45. C-MYB-FOXM1 TF pair
46. Time course of FOXM1 and c-MYB silencing
47. Common targets overexpressed in GC
48. The functions of the proteins in the interactome
49. preRC proteins interact with BUBR1 and AURKA
50. Conclusions and reflections
51. Acknowledgments

Topics Covered

• The evolution of systems biology
• Dissecting molecular interaction networks
• Algorithm for the Reverse-Engineering of Accurate Cellular Networks (ARACNe)
• Computing the mutual information
• Application to the MYC proto-oncogene
• Can ARACNe be applied to signaling networks
• Experimental data
• Dataset composition
• Discrete data handling
• EGFR network
• Modulator Inference by Network Dynamics (MINDy)
• Multivariate dependency model
• Conditional interactions
• STK38
• Top MYC modulators
• Signalome transfactome network
• Biochemical validation
• Bayesian evidence integration algorithm (BEIA)
• Interactome validation
• Interactome-Dysregulation Enrichment Analysis (IDEA)
• The "barcode" of cancer
• Master regulator analysis (MRA)
• The germinal center
• Shadowing and synergy

Links

Series:

• Microarrays

Categories:

• Cancer
• Methods

Therapeutic Areas:

• Oncology

Talk Citation

Califano, A. (2009, July 1). Cancer systems biology: dissection and analysis of context-specific molecular interaction networks [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 21, 2024, from https://doi.org/10.69645/YKYY3669.
Export Citation (RIS)

Publication History

• Published on July 1, 2009
• Archived on February 25, 2021

Financial Disclosures

• Prof. Andrea Califano has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.