Gene expression analysis summary
Where are we now?
Sample PreparationHybridization
Array designProbe design
Experimental Design
Buy standardChip / Array
Statistical AnalysisFit to Model (time series)
Expression IndexCalculation
Advanced Data AnalysisClustering PCA Gene Annotation Analysis Promoter Analysis
Classification Meta analysis Survival analysis Regulatory Network
ComparableGene Expression Data
Normalization
Image analysis
The DNA Microarray Analysis PipelineQuestion/hypothesis
DNA microarray analysis
PCA (using SVD)Cluster analysis
Normalization
Before
After
High-throughput applications of microarrays
• Gene expression *• DNA re-sequencing (relative to reference) *• SNP analysis *• Competitive growth assays *• ChIP-chip (interaction data) *• Array CGH• Whole genome tiling arrays• Peptide arrays (interaction data, not DNA based)*• De novo DNA sequencing (short)
Tiling microarrays
Huber W, et al., Bioinformatics 2006
Motivation for Systems Biology
Interest in Systems Biology?
Human genome completed
Pub
Med
abs
trac
ts
Systems biology and emerging properties
Transcriptional regulation of the Cell Cycle
Simon et al. Cell 2001
Boehringer Mannheim metabolic map
Mathematical abstraction of biochemistry
Metabolic models
“Genome scale” metabolic models
• Genes 708• Metabolites 584
– Cytosolic 559– Mitochondrial 164– Extracellular 121
• Reactions 1175– Cytosolic 702– Mitochondrial 124– Exchange fluxes 349
Forster et al. Genome Research 2003.
One framework for Systems Biology
1. The components. Discover all of the genes in the genome and the subset of genes, proteins, and other small molecules constituting the pathway of interest. If possible, define an initial model of the molecular interactions governing pathway function (how?).
2. Pathway perturbation. Perturb each pathway component through a series of genetic or environmental manipulations. Detect and quantify the corresponding global cellular response to each perturbation.
One framework for Systems Biology
3. Model Reconciliation. Integrate the observed mRNA and protein responses with the current, pathway-specific model and with the global network of protein-protein, protein-DNA, and other known physical interactions.
4. Model verification/expansion. Formulate new hypotheses to explain observations not predicted by the model. Design additional perturbation experiments to test these and iteratively repeat steps (2), (3), and (4).
From model to experiment and back again