Introduction to molecular networks Sushmita Roy BMI/CS Nov 6 th, 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 genes Circles 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 gene Edges: 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 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 Metabolites N M a b c O Enzymes d O M N Undirected, weighted Vertices: enzymes Edges: Enzyme M and N share a metabolite

Signaling networks Sachs et al., 2005, Science Receptors P Q A TF A P Q Directed Vertices: Enzymes and other proteins Edges: 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 HSP12 Sko1 Hot1 Sko1 Structure HSP12 Hot1 Who are the regulators? ψ(X 1,X 2 ) Function X1X1 X2X2 Y BOOLEAN LINEAR DIFF. EQNS PROBABILISTIC …. How they determine expression levels? Hot1 regulates HSP12 HSP12 is a target of Hot1

Mathematical representations of networks X1X1 X2X2 X3X3 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 NetworksDifferential equationsProbabilistic graphical models X1X X Input Output X1X2 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