introduction to molecular networks sushmita roy bmi/cs 576 [email protected] nov 6 th, 2014
TRANSCRIPT
Introduction to molecular networks
Sushmita RoyBMI/CS 576
www.biostat.wisc.edu/[email protected]
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