Network Analysis Goal: to turn a list of genes/proteins/metabolites into a network to capture insights about the biological system 1.Types of high-throughput data amenable to network analysis 2.Network theory and its relationship to biology 1

Physical Interactions: protein-protein interactions Data from: 1. Large-scale yeast-two hybrid assay: recovers binary (1:1) interactions 2. Protein immunoprecipitation & mass-spec identification: recovers complexes Giorgini & Muchowski Gen. Biol PEPTIDE TAG mass spectrometry to identify recovered proteins 3. Literature curation 2

Protein Arrays Proteins or antibodies immobilized onto a solid surface Antibody arrays: for identification & quantification of fluorescently labeled proteins in complex mixtures … proteins bind to immobilized Ab. Functional arrays: for measuring protein function * ppi: detect binding of fluorescent protein to immobilized peptides/proteins * kinase targets: detect phosphorylation of immobilized peptides/proteins by query kinase * ligand binding: detect DNA/carbohydrate/small molecule bound to immobilized proteins Reverse-phase arrays (lysate arrays): cells lysed in situ and immobilized cell lysate is screened 3

From Hall, Ptacek, & Snyder review 2007 Challenges: 1. Large-scale protein purification 2. Protein structure/stability requirements vary widely (unlike DNA) 3. Conditions for protein function vary widely 4. Protein epitope/binding domain must be displayed properly 4

High-throughput identification of gene/protein function: Functional Genomics Gene knock-out libraries: library of single-gene deletions for every gene done in yeast, E. coli, other fungi/bacteria S. cerevisiae libraries: heterozygous deletion (nonessential genes) OR homozygous deletion of all genes. * Each gene replaced with a short, unique ‘barcode’ sequence Strains can be phenotyped individually (screening) OR Selected for particular phenotypes – Strains surviving the selection can be readout on DNA arrays designed against the barcode sequences 5

Yeast deletion library used to: a) Identify ‘essential’ yeast genes and genes required for normal growth a) Genes required for survival of particular conditions/drugs b) Features of functional genomics, gene networks, etc * Screened deletion libraries for >700 conditions * Found ‘phenotype’ for nearly all yeast genes * Characterized which genes could be functionally profiled by which assays (e.g. phenotype, gene expression, etc) 6

Challenges: 1. Difficult to probe ‘essential’ and slow-growing strains 2. Cells likely to pick up secondary mutations to complement missing gene (chromosomal anueploidy in yeast) 7

High-throughput identification of gene/protein function: Functional Genomics RNAi knock-down libraries (C. elegans, flies, humans) Image from David Shapiro Small double-stranded DNAs complementary to mRNA can be injected (or fed) … … these are targeted by the RNAi pathway to inhibit mRNA stability/translation of target gene … knocks down protein abundance/function Challenges: 1. Doesn’t work for all genes/ds DNAs 2. Doesn’t work in all tissues 3. Delay in protein decrease, timing different for different proteins 8

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Insights from whole-genome knockout / knockdown studies * Screens for genes important for specific phenotypes/processes * Identifying off-target drug effects * Clustering of genes based on common phenotypes from knockdowns * Clustering/analysis of phenotypes with similar underlying genetics/processes * Integrative analysis with genomic expression, etc * Network analysis 10

Network structures Random network: Each node has roughly equal number of connections k, distributed according to Poisson distribution Scale-free network: Some nodes with few connections, other nodes (‘hubs’) with many connections (distributed according to Power Law) Directed vs. Undirected Graphs 11

Network Terminology Connectivity (degree) k: number of connections of a given node (average degree of all nodes ) Degree distribution: probability that a selected node has k connections Shortest path l: fewest number of links connecting two given nodes (average shortest path between all node pairs) Clustering coefficient: # of links connecting the k neighbors of Node X together 12

Scale-free Networks Connectivity: most nodes have few connections but joined by ‘hub’ nodes with many connections 13 ‘Small world’ effect: each node can be connected to any other node through relatively few connections ‘Disassortative’: hubs tend NOT to directly connect to one another ‘Robust’: network structure remains despite node removal (up to 80% removal!) ‘Hub vulnerability’: network structure is particularly reliant on few nodes (hubs)

14 Networks Challenges 1.Identifying relevant subnetworks 2.Integrating multiple data types (see #1 above) 3.Capturing temporary interactions and dynamic relationships 4.Using network structure/subnetworks to infer new insights about biology Networks Challenges 1.Infer hypothetical functions based on network connectivity 2.Reveal new connections between functional groups and complexes 3.Identify motifs and understand motif behaviors (more next time)

A gazillion plugins for Cytoscape …