Biological Networks

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Biological Networks
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Can a biologist fix a radio?
Lazebnik, Cancer Cell, 2002
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Building models from parts lists
Lazebnik, Cancer Cell, 2002
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Computational tools are needed to distill
pathways of interest from large molecular
interaction databases
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Jeong et al. Nature 411, 41 - 42 (2001)
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Different types of Biological Networks
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Network Representation
regulates
regulatory interactions (protein-DNA)
gene B
gene A
binds
functional complex (protein-protein)
protein A
Protein B
Enzymatic reaction
Metabolite B
metabolic pathways
Metabolite A
node
edge
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Network Analysis
Path
Hub
Clique
node
edge
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Scale Free vs Random Networks
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Small-world Network

• Every node can be reached from every other by a
  small number of steps
• Social networks, the Internet, and biological
  networks all exhibit small-world network
  characteristics

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What can we learn from a network?
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Searching for critical positions in a network ?
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Searching for critical positions in a network ?
High degree
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Searching for critical positions in a network ?
High degree
High closeness
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Searching for critical positions in a network ?
High degree
High closeness
High betweenness
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Features of cellular Networks
Hubs are highly connected nodes

• hubs tend not to interact directly with other
  hubs.
• Hubs tend to be older proteins
• Hubs are evolutionary conserved

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In a scale free network more proteins are
connected to the hubs
Albert et al. Science (2000) 406 378-382
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In yeast, only 20 of proteins are lethal when
deleted
Jeong et al. Nature 411, 41 - 42 (2001)
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Networks can help to predict function
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Mapping the phenotypic data to the network

• 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

Begley TJ, Mol Cancer Res. 2002
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Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
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Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
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Networks can help to predict function
Begley TJ, Mol Cancer Res. 2002.
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Finding Local properties of Biological Networks
Network Motifs

• Network motifs are recurrent circuit elements.
• We can study a network by looking at its parts
  (or motifs)
• How many motifs are in the network?

Adapted from An introduction to systems
biology by Uri Alon
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Finding Local properties of Biological Networks
Motifs
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Finding Local properties of Biological Networks
Motifs
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Finding Local properties of Biological Networks
Motifs
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Finding Local properties of Biological Networks
Motifs
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Finding Local properties of Biological Networks
Motifs

• What are these motifs?
• What biological relevance they have?

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Autoregulatory loop

• The probability of having autoregulatory loops in
  a random network is 0 !!!!.
• Transcription networks The regulation of a gene
  by its own product.
• Protein-Protein interaction network dimerization

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Autoregulatory loop
What is the effect of Autoregulatory loops on
gene expression levels?

• Positive autoregulation
• Fast time-rise of protein level

• Negative autoregulation
• Stable steady state

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Three-node loops
There are 13 possible structures with 3 nodes

• But in biological networks you can find only 2!

Feed forward loop
Feedback loop
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Feedback loop
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Course Summary
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What did we learn

• Pairwise alignment
• Local and Global Alignments

When? How ? Tools for local blast2seq ,
for global best use MSA tools such as
Clustal X, Muscle
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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

Tools Clustal X (for DNA and RNA), MUSCLE
(for proteins) Tools for phylogenetic trees
PHYLIP
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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
• - PHIBLAST
• Searching for a short pattern within a
  protein

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What did we learn

• Motif search
• When? How ?
• - Searching for known motifs in a given
  promoter (JASPAR)
• -Searching for overabundance of unknown
  regulatory motifs in a set of sequences e.g
  promoters of genes which have similar expression
  pattern (MEME)

Tools MEME, logo, Databases of motifs
JASPAR (Transcription Factors binding
sites) PRATT in PROSITE (searching for motifs in
protein sequences)
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What did we learn

• Protein Function Prediction
• When? How ?
• - Pfam (database to search for protein
  motifs/domain (PfamA/PfamB)
• - PROSITE
• - Protein annotations in UNIPROT
• (SwissProt/ Tremble)

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What did we learn

• Protein Secondary Structure Prediction-
• When? How ?
• Helix/Beta/Coil(PHDsec,PSIPRED).
• Predicts transmembrane helices (PHDhtm,TMHMM).
• Solvent accessibility important for the
  prediction of ligand binding sites (PHDacc).

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What did we learn

• Protein Tertiary Structure Prediction-
• When? How ?
• First we must look at sequence identity to a
  sequence with a known structure!!
• Homology modeling/Threading
• MODEBase- database of models
• Remember Low quality models can be miss leading
  !!
• Tools SWISS-MODEL ,genTHREADER, MODEBase

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What did we learn

• RNA Structure and Function Prediction-
• When? How ?
• RNAfold good for local interactions, several
  predictions of low energy structures
• Alifold adding information from MSA
• RFAM
• Specific database and search tools tRNA,
  microRNA ..

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What did we learn

• Gene expression
• When? How ?
• Many database of gene expression
• GEO
• Clustering analysis
• EPClust (different clustering methods K-means,
  Hierarchical Clustering, trasformations
  row/columns/both)
• GO annotation (analysis of gene clusters..)

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So How do we start

• Given a hypothetical sequence predict it
  function.

What should we do???
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Example

• Amyloids are proteins which tend to aggregate in
  solution. Abnormal accumulation of amyloid in
  organs is assumed to play a role in various
  neurodegenerative diseases.
• Question can we predict whether a protein X is
  an amyolid ?