chapter 6


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Inferring Regulatory Pathways Bayesian Networks Model Based Approach Additional Biological Knowledge Data Kin Z Kin Z Kin TF XZ Kin TF XZ TF X TF X TF Y TF X TF Y TF X TF Y TF X TF Y TF X Gene A Gene A Gene A Gene A Gene A Gene A Gene A Gene A USV1 Hap4 Interpretation Reconstruction Msn2 Hap4 Atp16 What is a model Step 1 define class of potential models stochastic Step 2 reconstruct a specific model A description of a process that could have Step 3 visualization testable generated the observed data hypotheses Emphasis on the choice of model and how to use it The data manipulation steps are derived from the model 1 Ra f PK A Er k p3 8 PK C P IP Jn 2 k P IP P lc 3 Ak t From Data to Signaling Pathways Cell1 Computational analysis Cell3 Cell4 Flow cytometry Cell2 Cell x Automated de novo reconstruction from high throughput data Primary Human T Lymphocyte Data perturbation a perturbation b 12 Color Flow Cytometry Ra Mef k Er 1 2 k p3 8 PK A PK C Jn k PIP 2 PIP Plc 3 Ak t Conditions 96 well format Datasets of cells condition a condition b condition n perturbation n Simultaneous information measured individual primary human cells Thousands of data points 2 Interventions on a known map 1 2 CD3 3 LFA 1 CD28 L A T Zap70 RAS PI3K VAV SLP 76 Lck 10 Cytohesin JAB 1 4 6 PKC PLC Akt PIP3 5 7 Activators 1 CD3 2 CD28 3 ICAM 2 4 PMA 5 2cAMP PIP2 PKA Raf MAPKKK MAPKKK Mek1 2 MEK4 7 MEK3 6 Erk1 2 JNK p38 8 9 Inhibitors 6 G06976 7 AKT inh 8 Psitect 9 U0126 10 LY294002 Using Correlations PKC PKA Plc Phospho Proteins Phospho Lipids Raf Jnk Mek P38 Erk PIP3 Akt PIP2 3 Bayesian Network Approach Protein A Protein B Protein C Protein E Protein D Correlations Node Measured between level activitycellular of proteincomponents represent interactions Edge Influence between proteinsand influences Statistical Dependencies A B C E D But how can statistical dependencies lead to causality 4 Causality Who Influences Whom No Manipulations A inhibited Phospho A B inhibited B B A A Phospho B Some Real Data

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