Biological Networks

Can a biologist fix a radio?

Lazebnik, Cancer Cell, 2002

Building models from parts lists

Protein-DNAinteractions

Gene levels(up/down)

Protein-proteininteractions

Protein levels(present/absent)

Biochemicalreactions

Biochemicallevels

▲ Chromatin IP ▼ DNA microarray

▲ Protein coIP▼ Mass spectrometry

▲noneMetabolic flux ▼

measurements

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

Data integration and statistical mining

Types of information to integrate• Data that determine the network (nodes and edges)

– protein-protein– protein-DNA, etc…

• Data that determine the state of the system– mRNA expression data– Protein levels– Dynamics over time

Networks can help to predict function

Mapping the phenotypic data to the network

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

Mapping the phenotypic data to the network

Begley TJ, Mol Cancer Res. 2002

Mapping the phenotypic data to the network

Begley TJ, Mol Cancer Res. 2002

Networks can help to predict function

Begley TJ, Mol Cancer Res. 2002.

Networks Topology

gene A gene Bregulates

gene A gene Bbinds

gene A gene B

reaction product

is a substrate for

regulatory interactions(protein-DNA)

functional complexB is a substrate of A

(protein-protein)

metabolic pathways

Network Representation

node edge

Paths:metabolic, signaling pathways

Cliques:protein complexes

Hubs:regulatory modules

Network Analysis

node edge

Small-world Network

• Social networks, the Internet, and biological networks all exhibit small-world network characteristics

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

Shortest-Path between nodes

Shortest-Path between nodes

Longest Shortest-Path

Small-world Network

• Social networks, the Internet, and biological networks all exhibit small-world network characteristics

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

• Small World Networks are characterized by high clustering coefficient and low mean-

shortest path length

Scale Free Networks

Scale-Free Networks are Robust• Complex systems (cell, internet, social

networks), are resilient to component failure

• Network topology plays an important role in this robustness– Even if ~80% of nodes fail, the remaining ~20% still maintain

network connectivity– Network is very sensitive if the hubs are “attacked”

• In yeast, only ~20% of proteins are lethal when deleted,

Features of cellular Networks

• Cellular networks are assortative, hubs tend not to interact directly with other hubs.

• Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only)

• Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)

Looking at macromolecular structures as a network

How to Indentify critical position in the newtwork?

Searching for critical positions in a network ?

Searching for critical positions in a network ?

High degree

Searching for critical positions in a network ?

High closeness

High degree

Searching for critical positions in a network ?

High closeness

High degree

High betweenness

Looking at macromolecular structures as a network

A1191 = highest degree, closeness, betweenness

A1191

Identifying Deleterious Mutationsusing a network approach

Strong mutations

Mild mutations

12

Identifying Deleterious Mutations

p~0

p~0

p=0.01

There is a significant overlap between (predicted) functional nucleotides and critical positions of the network (high betweenness and high closeness