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Printable Handouts
Navigable Slide Index
- Introduction
- Talk outline
- The evolution of systems biology
- Dissecting molecular interaction networks
- ARACNe
- Reverse engineering regulatory networks
- Computing the mutual information
- How ARACNe works
- Application to the MYC proto-oncogene
- Can ARACNe be applied to signaling networks?
- Experimental data for ARACNe
- Dataset composition
- Discrete data handling: ARACNe modification
- ARACNe: EGFR network
- MINDy- Modulator inference by network dynamics
- Reverse engineering conditional interactions
- Reverse engineering conditional TF interactions
- Multivariate dependency model
- A transistor-like 3-way interaction model
- MINDy: conditional interactions
- Silencing of STK38 affects c-Myc turn-over
- STK38 affects MYC localization and activity
- The effects of BHLHB2 on TERT transcription
- Top MYC modulators identified by MINDy
- Signalome transfactome network
- Biochemical validation
- BEIA: Bayesian evidence integration algorithm
- Interactome validation
- IDEA
- IDEA workflow
- Interaction dysregulation analysis
- The "barcode" of cancer
- IDEA scoring
- What can IDEA identify?
- Dysregulated genes identify dysregulated pathway
- Master regulators of human phenotypes
- The germinal center
- The idea behind MRA
- Differential activity of TFs
- Shadow regulation and synergy
- Statistical tests for shadowing and synergy
- Shadow removal
- 37 GC-activated pairs (16 TF)
- Top activated MRs
- C-MYB-FOXM1 TF pair
- Time course of FOXM1 and c-MYB silencing
- Common targets overexpressed in GC
- The functions of the proteins in the interactome
- preRC proteins interact with BUBR1 and AURKA
- Conclusions and reflections
- 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
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 December 6, 2024, from https://doi.org/10.69645/YKYY3669.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Andrea Califano has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Cancer systems biology: dissection and analysis of context-specific molecular interaction networks
A selection of talks on Oncology
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