Introduction to molecular networks

Sushmita RoyBMI/CS 576

www.biostat.wisc.edu/bmi576sroy@biostat.wisc.edu

Nov 6th, 2014

Understanding a cell as a system

• Measure: identify the parts of a system– Parts: different types of bio-molecules

• genes, proteins, metabolites– High-throughput assays to measure these molecules

• Model: how these parts are put together– Clustering– Network inference and analysis

Omics data provide comprehensive description of nearly all components of the cell

Joyce & Palsson, Nature Mol cell biol. 2006

Key concepts in networks

• What are molecular networks?– Different types of networks– Graph-theoretic representation

• Computational problems in network biology– Network structure and parameter learning– Analysis of network properties– Inference on networks: for data integration, interpretation and

hypothesis generation

• Classes of methods for expression-based network inference• Probabilistic graphical models for networks

– Bayesian networks and dependency networks – Evaluation of inferred networks

A network

• Describes connectivity patterns between the parts of a system– Vertex/Nodes: parts– Edges/Interactions: connections

• Edges can have sign and/or weight• Connectivity is represented as a graph

– Node and vertex are used interchangeably– Edge and interaction are used interchangeably

Vertex/Node

Edge

A

B C

D

E

F

Why are networks important?

• Genes do not function independently• Identifying these networks is a central challenge in Systems

biology• Motivating applications

– Protein Networks for cancer prognosis– Networks for interpretation of genetic mutants– Networks for gene prioritization

Protein networks for predicting cancer prognosis

Diamonds are differentially expressed genesCircles are not differentially expressed but important for predictive power

Chuang & Ideker, Annual Reviews 2010

Different types of molecular networks

• Depends on what – the vertices represent– the edges represent– whether edges directed or undirected

• Molecular networks– Vertices are bio-molecules

• Genes, proteins, metabolites– Edges represent interaction between molecules

Transcriptional regulatory networks

157 TFs and 4410 target genes

E. coli: 153 TFs and 1319 target genes

S. cerevisiae: Vargas and Santillan, 2008

Nodes: regulatory protein like a TF, or target geneEdges: TF A regulates C

A B

Gene C

Transcription factors (TF)

C

A B

Directed, Signed, weighted

Detecting protein-DNA interactions• ChIP-chip and ChIP-chip binding profiles for transcription factors• Determine the (approximate) locations in the genome where a protein binds

Peter Park, Nature Reviews Genetics, 2009

Protein-protein interaction networks

Barabasi et al. 2004

Yeast protein interaction network

Vertices: proteins Edges: Protein U physically interacts with protein X

U X

Y Z

Protein complex

U

Y

X

Z

Undirected

Proteins (enzymes)metabolites

Figure from KEGG database

Metabolic networks

MetabolitesN

Ma b

c

O

Enzymes

d

O

M N

Undirected, weighted

Vertices: enzymesEdges: Enzyme M and N share a metabolite

Signaling networks

Sachs et al., 2005, Science

Receptors

P

Q

ATF

A

P

Q

Directed

Vertices: Enzymes and other proteinsEdges: Enzyme P modifies protein Q

Genetic interaction networks

Dixon et al., 2009, Annu. Rev. Genet

Q G

Genetic interaction: If the phenotype of double mutant is significantly different than each mutant along

Undirected

Vertices: Genes Edges: Genetic interaction between query (Q) and gene G

Yeast genetic interaction network

Costanzo et al, 2011, Science

Summary of different types of Molecular networks

• Physical networks – Transcriptional regulatory networks: interactions between

regulatory proteins (transcription factors) and genes– Protein-protein: interactions among proteins– Signaling networks: interactions between protein and small

molecules, and among proteins that relay signals from outside the cell to the nucleus

• Functional networks– metabolic: describe reactions through which enzymes convert

substrates to products– genetic: describe interactions among genes which when

genetically perturbed together produce a significant phenotype than individually

Computational problems in networks

• Network reconstruction– Infer the structure and parameters of networks– We will examine this problem in the context of “expression-based

network inference”

• Network evaluation – Properties of networks

• Network applications– Interpretation of gene sets– Using networks to infer function of a gene

Network reconstruction

• Given – A set of attributes associated with network nodes– Typically attributes are mRNA levels

• Do– Infer what nodes interact with each other

• Algorithms for network reconstruction can vary based on their meaning of interaction– Similarity– Mutual information– Predictive ability

Computational methods to infer networks

• We will focus on transcriptional regulatory networks• These networks are inferred from gene expression data• Many methods to do network inference

– We will focus on probabilistic graphical models

Modeling a regulatory network

HSP12Sko1Hot1

Sko1

Structure

HSP12

Hot1

Who are the regulators?

ψ(X1,X2)

Function

X1 X2

Y

BOOLEANLINEARDIFF. EQNSPROBABILISTIC…

.

How they determine expression levels?

Hot1 regulates HSP12

HSP12 is a target of Hot1

Mathematical representations of networks

X1 X2

X3

f

Output expression of node

Models differ in the function that maps input system state to output state

Input expression of neighbors

Rate equations Probability distributions

Boolean Networks Differential equations Probabilistic graphical models

X1 X2

0 0

0 1

1 0

1 1

X3

0

1

1

1

Input OutputX1 X2

X3

Network evaluation

• How accurate is the network?– In silico validation– Agreement with known interactions– Agreement with experiments

• Do topological properties of the network represent important biological functions– Degree distributions– Network motifs– Highly connected nodes and relationship to lethality

Network applications

• Interpretation of gene sets (for example from clustering)• Integration of two or more different types of datasets• Graph clustering• Classification/Inference on graphs

Plan for next lectures

• Overview of Expression-based network inference– Classes of methods– Strengths and weaknesses of different methods

• Representing networks as probabilistic graphical models– Bayesian networks– Module networks– Dependency networks

• Evaluation of inferred networks