Biological Networks Can a biologist fix a radio

Biological Networks

Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002

Building models from parts lists

Protein-DNA interactions ▲ Chromatin IP ▼ DNA microarray Gene levels (up/down) Protein-protein interactions ▲ Protein co. IP ▼ Mass spectrometry Protein levels (present/absent) Biochemical reactions ▲none Metabolic flux ▼ measurements Biochemical levels

Data integration and statistical mining Computational tools are needed to distill pathways of interest from large molecular interaction databases

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 – m. RNA expression data – Protein levels – Dynamics over time

Networks can help to predict function

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

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

Network Representation gene A node regulates binds reaction product is a substrate for gene B regulatory interactions (protein-DNA) gene B functional complex B is a substrate of A (protein-protein) gene B metabolic pathways edge

Network Analysis Paths: metabolic, signaling pathways Cliques: protein complexes Hubs: regulatory modules 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

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 meanshortest 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 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 degree High closeness

Searching for critical positions in a network ? High degree High closeness High betweenness

Looking at macromolecular structures as a network A 1191 = highest degree, closeness, betweenness

Identifying Deleterious Mutations using a network approach 2 Strong mutations Mild mutations 1

Identifying Deleterious Mutations 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