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Biological Networks

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Biological Networks. Can a biologist fix a radio?. Lazebnik, Cancer Cell, 2002. Building models from parts lists. Lazebnik, Cancer Cell, 2002. Building models from parts lists. Computational tools are needed to distill pathways of interest from large molecular interaction databases. - PowerPoint PPT Presentation

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Page 1: Biological Networks

Biological Networks

Page 2: Biological Networks

Can a biologist fix a radio?

Lazebnik, Cancer Cell, 2002

Page 3: Biological Networks

Building models from parts lists

Lazebnik, Cancer Cell, 2002

Page 4: Biological Networks

Building models from parts lists

Page 5: Biological Networks

Computational tools are needed to distill pathways of interest from large molecular interaction databases

Thinking computationally about biological process may lead to more accurate models,which in turn can be used to improve the design of algorithms

Navlakha and Bar-Joseph 2011

Page 6: Biological Networks

Jeong et al. Nature 411, 41 - 42 (2001)

Biological Networks

Page 7: Biological Networks

Proteins

Physical Interaction

Protein-Protein

A

B

Protein Interaction

Transcription factorTarget genes

TranscriptionalInteraction

Protein-DNA

A

B

Transcriptional

Different types of Biological Networks

Nodes

Edges

Page 8: Biological Networks

What can we learn from the topology of biological networks

• Hubs tend to be “older” proteins

• Hubs are evolutionary conserved

Hubs are highlyconnected nodes

Are hubs functionally important ?

Page 9: Biological Networks

Hubs are usually critical proteins for the species

LethalSlow-growthNon-lethalUnknown

Jeong et al. Nature 411, 41 - 42 (2001)

Page 10: Biological Networks

Networks can help to predict function

Page 11: Biological Networks

Can the network help to predict function

Begley TJ, Mol Cancer Res. 2002

•Systematic phenotyping of 1615 gene knockout strains in yeast•Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents)•Screening against a network of 12,232 protein interactions

Page 12: Biological Networks

Mapping the phenotypic data to the network

Begley TJ, Mol Cancer Res. 2002

Page 13: Biological Networks

Mapping the phenotypic data to the network

Begley TJ, Mol Cancer Res. 2002

Page 14: Biological Networks

Networks can help to predict function

Begley TJ, Mol Cancer Res. 2002.

Page 15: Biological Networks

A network approach to predict new drug targets

Aim :to identify critical positions on the ribosome which could be potential

targets of new antibiotics

Page 16: Biological Networks

Keats (1795-1821) Kafka (1883-1924) Orwell (1903-1950)

Mozart (1756-1791) Schubert (1797-1828) Chopin (1810-1849)

Page 17: Biological Networks

In our days…

Infectious diseases are still number 1 cause of premature death

(0-44 years of age) worldwide..

Annually kill >13 million people (~33% of all deaths)

Page 18: Biological Networks

Antibiotics targets of the large ribosomal subunit

The ribosome is a target for approximately half of antibiotics characterized to date

Page 19: Biological Networks

Looking at the ribosome as a network

A1191

Page 20: Biological Networks

Many biological network have Many biological network have characteristics of acharacteristics of a

Small World NetworkSmall World Network

Every node can be reached from every other by a small number of steps

Page 21: Biological Networks

Does the ribosome network have characteristics of a

Small World Network?

SWN Ribosome Graph

) L (Average Path Length

8.5 11.9

)C (Clustering Coefficient

0.63 0.42

Page 22: Biological Networks

What can we learn from the ribosome network?

1. Critical sites in the ribosome network may represent functional sites

(not discovered before)

2. New functional sites may be good sites for drug design

Page 23: Biological Networks

Looking for critical positions in a networkLooking for critical positions in a network

Page 24: Biological Networks

Looking for critical positions in a networkLooking for critical positions in a networkDegree: the number of edges that a node has.

The node with the highest degree in the graph (HUB)

Page 25: Biological Networks

Degree: the number of edges that a node has.

The node with the highest degree in the graph (HUB)

Looking for critical positions in a networkLooking for critical positions in a network

Page 26: Biological Networks

Closeness (centrality)Closeness (centrality)Closeness: measure how close a node to all other nodes in the network.

The nodes with the highest closeness

Page 27: Biological Networks

Betweenness (connectivity)Betweenness (connectivity)

The node with the highest betweenness

Betweenness: quantify the number of all shortest paths that pass through a node.

Page 28: Biological Networks

The node with the highest degree

The node with the highest betweenness

The nodes with the highest closeness

Looking for critical positions in a networkLooking for critical positions in a network

Page 29: Biological Networks

Looking at macromolecular structures as a Looking at macromolecular structures as a networknetwork

A1191

A1191 have the highest closeness, betwenness, and degree.

Page 30: Biological Networks

Which (is there a?) property best characterizes

the known function sites?

How can the network approach help How can the network approach help identify functional sites in the identify functional sites in the

ribosome ? ribosome ?

Characterize the whole ribosome as a network

Calculate the network properties of each nucleotide

?

Page 31: Biological Networks

Lethal mutations

Neutral mutations

12

When mutating the critical site on the When mutating the critical site on the ribosomeribosome

the bacteria will not grow the bacteria will not grow

Page 32: Biological Networks

Critical site on the ribosomeCritical site on the ribosomehave very high centrality values have very high centrality values

(closeness)(closeness)

Lethal Mutations Neutral Mutations

David-Eden and Mandel-Gutfreund, 2008

nucleotides with the highest

closeness

nucleotideswith the highest

closeness

P-value~0 P-value=1

Page 33: Biological Networks

Critical site on the ribosomeCritical site on the ribosomehave very high connectivity have very high connectivity

(betweenness)(betweenness)

Lethal Mutations Neutral Mutations

David-Eden et al, 2008

nucleotides with the highest

betweennes

nucleotideswith the highest

betweennes

P-value~0 P-value=1

Page 34: Biological Networks

p~0

p~0

p=0.01

Critical site on the ribosomeCritical site on the ribosomehave unique network properties have unique network properties

Lethal mutations Neutral mutations

David-Eden et al, NAR (2008)

Page 35: Biological Networks

‘ ‘Druggability Index’Druggability Index’Based on the network propertyBased on the network property

David-Eden et al. NAR (2010)

Bad site Good site

Page 36: Biological Networks

Pockets with the highest ‘Druggability Pockets with the highest ‘Druggability Index’Index’

overlap known drug binding siteoverlap known drug binding sitess

David-Eden et al. NAR (2010)

DI=1 DI=0.98

Erythromycin Telithromycin

Girodazole

DI=0.94 DI=0.93

Page 37: Biological Networks

Course Summaryand

How to start working on your project

Page 38: Biological Networks

What did we learn

• Pairwise alignment – Dynamic Programing

Local and Global Alignments

When? How ?

Recommended Tools : for local alignment blast2seqlast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&PROG_DEF=blastn&BLAST_PROG_DEF=megaBlast&BLAST_SPEC=blast2seq

For global best use MSA tools such as Clustal W2, Muscle (see next slide)

Page 39: Biological Networks

What did we learn• Multiple alignments (MSA)

When? How ?

MSA are needed as an input for many different purposes: searching motifs, phylogenetic analysis, protein and RNA structure predictions, conservation of specific nts/residues

Recommended Tools : Clustal W2 http://www.ebi.ac.uk/Tools/msa/clustalw2/ (best for DNA and RNA), MUSCLE http://www.drive5.com/muscle/ (best for proteins)Phylogeny.fr phylogenetic trees http://www.phylogeny.fr/

Page 40: Biological Networks

What did we learn• Search a sequence against a database

When? How ? - BLAST :Remember different option for BLAST!!! (blastP blastN…. ), make

sure to search the right database!!!

DO NOT FORGET –You can change the scoring matrices, gap penalty etc

- PSIBLAST

Searching for remote homologies

BLAST http://blast.ncbi.nlm.nih.gov/Blast.cgi

Page 41: Biological Networks

What did we learn >Motif search

When? How ?

-Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME)

>Domain search

Pfam (database to search for protein domains)

Suggested Tools : MEME http://meme.nbcr.net/meme/DRIMUST http://drimust.technion.ac.il/

PFAM http://pfam.sanger.ac.uk/

Page 42: Biological Networks

What did we learn• Protein Secondary Structure Prediction-

When? How ?– Helix/Beta/Coil– Most successful approaches rely on

information from the environment and MSA

- Predictions level around 80%

Suggested toolsJpred: http://www.compbio.dundee.ac.uk/www-jpred/

Page 43: Biological Networks

What did we learn• Protein Tertiary Structure Prediction-

When? How ?– First we must look at sequence identity to a sequence with

a known structure!!– Sequence homology based methods-Homology modeling– Structure homology based methods- Threading

Remember : Low quality models can be miss leading !! Database and tools

Protein Data Bank http://www.rcsb.org/pdb/home/home.doSuggested tool for molecular visualization http://www.pymol.org/Good tool for homology modeling http://modbase.compbio.ucsf.edu/

Page 44: Biological Networks

What did we learn• RNA Structure and Function Prediction-

When? How ?– MFE based methods– good for local interactions, several

predictions of low energy structures

– Adding information from MSA can help but usually not available

– RNA families are characterized by their structure (Rfam).

Suggested tools: RNAfold http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi

RFAM http://rfam.sanger.ac.uk/

Page 45: Biological Networks

What did we learn• Gene expression

When? How ?> Unsupervised methods-

Different clustering methods : K-means, Hierarchical Clustering

> Supervised methods-such as SVM–GO annotation (analysis of gene clusters..)

Selected databases and toolsGEO http://www.ncbi.nlm.nih.gov/geo/EPclust http://www.bioinf.ebc.ee/EP/EP/EPCLUST/David http://david.abcc.ncifcrf.gov/

Page 46: Biological Networks

What did we learn?Biological Networks

• Different types of Biological Networks

Protein-Protein (non-directed)

Regulatory networks (directed)

structural networks

• Network Motifs

• Network Topology

Selected toolsString http://string-db.org/Biogrid http://thebiogrid.org/Cytoscape http://www.cytoscape.org/

Page 47: Biological Networks

Most useful databases

Genomic databaseThe human genome browser

http://genome.ucsc.edu/

Protein databaseUniprothttp://www.uniprot.org/

Structure databasePDB (RCSB)http://www.rcsb.org

Gene expression databaseGEOhttp://www.ncbi.nlm.nih.gov/geo/

Page 48: Biological Networks

So How do we start …Now that you have selected a project you should carefully plan your next steps:

A.Make sure you understand the problem and read the necessary background to proceed

B. formulate your working plan, step by step

C. After you have a plan, start from extracting the necessary data and decide on the relevant tools to use at the first step. When running a tool make sure to summarize the results and extract the relevant information you need to answer your question, it is recommended to save the raw data for your records , don't present raw data in your final project. Your initial results should guide you towards your next steps.

D. When you feel you explored all tools you can apply to answer your question you should summarize and get to conclusions. Remember NO is also an answer as long as you are sure it is NO. Also remember this is a course project not only a HW exercise. .

Page 49: Biological Networks

Preparing a poster

Prepare in PPT poster size 90-120 cmTitle of the project Names and affiliation of the students presenting

The poster should include 5 sections :Background should include description of your question (can add figure)Goal and Research Plan: Describe the main objective and the research planResults (main section) : Present your results in 3-4 figures, describe each figure (figure legends) and give a title to each result Conclusions : summarized in points the conclusions of your projectReferences : List the references of paper/databases/tools used for your project

Page 50: Biological Networks

Key date reminder

16.1 Submission project proposal20.1 Meetings with supervisors 19.3 Poster submission26.3 Poster presentation (POSTER DAY 12:30-14:30)