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