Networks

A series of entities or NODES (genes, proteins, metabolites, individuals, ecosystems, etc, etc) and the interactions or EDGESbetween them.

Directed graph(where connections have directionality,

e.g. kinase – substrate connections)

Undirected graph

Network Analysis

Goal: to turn a list of genes/proteins/metabolites into a network to capture insights about the biological system

Today:

1. Types of high-throughput data amenable to network analysis

2. Network theory and its relationship to biology

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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.

2005

PEPTIDE TAG

mass spectrometry to identify recovered proteins

3. Literature curation 3

Nature 2005

Y2H + literature curation

Protein ArraysProteins 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

5

From Hall, Ptacek, & Snyder review 2007

Challenges:

1. Large-scale protein purification

2. Protein structure/stability requirementsvary widely (unlike DNA)

3. Conditions for protein function vary widely

4. Protein epitope/binding domain must bedisplayed properly

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High-throughput identification of gene/protein function:Functional Genomics

Gene knock-out libraries: library of single-gene deletions for every genedone 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

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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) 8

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)

9

Science 2010

Pairwise deletionsto measure

genetic interactions for 75% of yeast

genes

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 mRNAcan 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 DNAs2. Doesn’t work in all tissues3. Delay in protein decrease, timing

different for different proteins

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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

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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 14

Network Terminology

Connectivity (Degree) k: number of connections of a given node

(average degree of all nodes <k>)

Degree distribution: probability that a selected node has k connections

Shortest path l: fewest number oflinks connecting two given nodes(average shortest path <l> between all node pairs)

Clustering coefficient: # of links connectingthe k neighbors of Node X together

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Scale-free Networks

Connectivity: most nodes have few connectionsbut joined by ‘hub’ nodes with many connections

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‘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 isparticularly reliant on few nodes (hubs)

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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)

http://www.cytoscape.org/

A gazillion plugins for Cytoscape …

KinaseTranscription FactorTarget Gene/Module

430 proteins1199 edges

Debbie Chasman & Mark Craven

Inferred NaCl-activated Signaling Network

starting network:5,855 proteins25,906 edges

Orthologs of human disease genes are enriched in the network

Disease-associated orthologHuman ortholog not linked to disease

430 proteins188 have one-to-one human orthologs

95% of ‘reviewed’orthologs are

disease associated