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Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks Mark B Gerstein Yale Slides at   Lectures.GersteinLab.org   (See Last Slide for References & More Info.)   (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
The problem: Grappling with  Function on a Genome Scale? ,[object Object],[object Object],[object Object],.……  ~530
Traditional single molecule way to integrate evidence & describe function Descriptive Name:  Elongation Factor 2 Summary sentence  describing function: This protein promotes the GTP-dependent translocation of the nascent protein chain from the A-site to the P-site of the ribosome.  EF2_YEAST Lots of references   to papers
Some obvious issues in scaling single molecule definition to a genomic scale ,[object Object],[object Object],[object Object],[object Object]
Some obvious issues in scaling single molecule definition to a genomic scale ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Seringhaus et al. GenomeBiology (2008)]
Hierarchies & DAGs of  controlled-vocab terms but still have issues...  [Seringhaus & Gerstein, Am. Sci. '08] GO (Ashburner et al.) MIPS (Mewes et al.)
Towards Developing Standardized Descriptions of Function ,[object Object],[object Object],[object Object],[object Object],Interaction Vectors  [Lan et al, IEEE 90:1848]
Networks (Old & New) [Seringhaus & Gerstein, Am. Sci. '08] Classical KEGG pathway Same Genes in High-throughput Network
Networks occupy a midway point in terms of level of understanding 1D: Complete  Genetic Partslist ~2D: Bio-molecular Network  Wiring Diagram 3D: Detailed structural  understanding of  cellular machinery [Jeong et al. Nature, 41:411] [Fleischmann et al., Science, 269 :496]
Networks as a universal language Disease Spread [Krebs] Protein Interactions [Barabasi] Social Network Food Web Neural Network [Cajal] Electronic Circuit Internet [Burch & Cheswick]
Networks as a Central Theme  in Systems Biology Reductionist Approach Integrative Approach [Adapted from H Yu] Systems Biology
Interactome networks Network  pathology & pharmacology [Adapted from H Yu] Breast Cancer Parkinson’s Disease Alzheimer’s Disease Multiple Sclerosis
Using the position in networks to describe function [NY Times, 2-Oct-05, 9-Dec-08]
Types of Networks Interaction networks [Horak, et al, Genes & Development, 16:3017-3033] [DeRisi, Iyer, and Brown, Science, 278:680-686] [Jeong et al, Nature, 41:411] Regulatory networks Metabolic networks Nodes: proteins or genes Edges: interactions
Combining networks forms an ideal way of integrating diverse information Metabolic pathway Transcriptional regulatory network Physical protein-protein Interaction Co-expression Relationship Part of the  TCA cycle Genetic interaction (synthetic lethal) Signaling pathways
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predicting Networks How do we construct large molecular networks?  From extrapolating correlations between functional genomics data with fairly small sets of known interactions, making best use of the known training data.
Network Prediction Figure 6: A map of known and a subset of predicted interactions among helical membrane proteins.  Nodes represent helical membrane proteins, and edges represent interactions among them.  Red edges represent known interactions that are also predicted to interact, blue edges represent other known interactions, and green edges represent ~700 top interaction predictions (ranked by descending logistic regression score) out of a total of 4,145.  Purple nodes represent helical membrane proteins that show up in the known interactions, and green nodes represent new helical membrane proteins. New K n o w n Ex. of Predicted Membrane Protein Interactome in Xia et al. JMB (2006) ,[object Object],[object Object],[object Object]
Example: yeast PPI network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Training sets Known interactions Known non-interactions Unknown 1 2 4 3 1 2 3 4 1 0 1 ? 1 2 1 ? 0 ? 3 ? 0 ? ? 4 1 ? ? ?
Features ,[object Object],x 1  = (0.2, 2.4, 1.5, …) x 2  = (0.8, 2.2, 1.5, …) x 3  = (4.3, 0.1, 7.5, …) … sim(x 1 , x 2 ) = 0.62 sim(x 1 , x 3 ) = -0.58 … Gasch et al., 2000 1 2 4 3 Similarity scale: 1 -1
Features ,[object Object],x 1  = (1, 1, 0, 0, …) x 2  = (1, 1, 1, 0, …) x 3  = (1, 0, 1, 0, …) … sim(x 1 , x 2 ) = 0.81 sim(x 1 , x 3 ) = 0.12 … Similarity scale: 1 -1 http://www.scq.ubc.ca/wp-content/yeasttwohybridtranscript.gif 1 2 4 3
Data integration & Similarity Matrix 1 2 4 3 1 2 4 3 1 2 4 3 1 2 4 3 1 2 3 4 1 1.00 0.57 0.55 0.40 2 0.57 1.00 0.66 0.89 3 0.55 0.66 1.00 0.79 4 0.40 0.89 0.79 1.00
Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Kernels ,[object Object],Objects in an feature space 2 1 3 4 Good for integrating heterogeneous datasets (protein sequences, PSSM, gene expression, …) –  no need to explicitly place them in a common feature space Similarity matrix Compute inner products p.s.d. implies
Kernel methods ,[object Object],[object Object],The only thing that we need to know about the objects: their similarity values (inner products) Maximize Subject to  Equivalent to 2 1 3 4
Kernel methods for predicting networks: local vs. global modeling ,[object Object],Model for node 3: Problem: insufficient and unevenly distributed training data (what if node 3 has no known interactions at all?) 1 2 4 3 ? 1 2 4 3 2 4
Kernel methods for predicting networks: local vs. global modeling ,[object Object],Pairwise kernel: consider object pairs instead of individual objects Problem: O(n 2 ) instances, O(n 4 ) kernel elements Direct methods: threshold the kernel to make predictions Problem: One single global model, may not be able to handle subclasses 1 2 4 3 ? 1 3 2 3 2 4 1 4 1 2 3 4 1 1 4 4 2 2 3 3 1 4 1 2 3 4 4 4 2 2 3 3 1 2 3 4 1 1.00 0.57 0.55 0.40 2 0.57 1.00 0.66 0.89 3 0.55 0.66 1.00 0.79 4 0.40 0.89 0.79 1.00 Threshold: 0.7 1 2 3 4 1 1.00 0.57 0.55 0.40 2 0.57 1.00 0.66 0.89 3 0.55 0.66 1.00 0.79 4 0.40 0.89 0.79 1.00
Our work: training set expansion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)]
Prediction propagation ,[object Object],[object Object],[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)] 1 2 4 3 1 2 4 3 1 2 4 3 1 2 4 3 1 2 4 3
Kernel initialization ,[object Object],[object Object],[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)] 1 2 4 3 1 2 3 4 1 1.00 0.57 0.55 0.40 2 0.57 1.00 0.66 0.89 3 0.55 0.66 1.00 0.79 4 0.40 0.89 0.79 1.00 1 2 4 3
Remarks ,[object Object],[object Object],[object Object],[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)]
Experiments ,[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)] Code Data type Source Kernel phy Phylogenetic profiles COG v7 (Tatusov et al., 1997) RBF (  =3,8) loc Sub-cellular localization (Huh et al., 2003) Linear exp-gasch Gene expression (environmental response) (Gasch et al., 2000) RBF (  =3,8) exp-spellman Gene expression (cell-cycle) (Spellman et al., 1998) RBF (  =3,8) y2h-ito Yeast two-hybrid (Ito et al., 2000) Diffusion (  =0.01) y2h-uetz Yeast two-hybrid (Uetz et al., 2000) Diffusion (  =0.01) tap-gavin Tandem affinity purification (Gavin et al., 2006) Diffusion (  =0.01) tap-krogan Tandem affinity purification (Krogan et al., 2006) Diffusion (  =0.01) int Integration Summation
Prediction accuracy ,[object Object],[object Object],[object Object],[object Object],[Yip and Gerstein, Bioinformatics ('09, in press)]
Complementarity of the two methods [Yip and Gerstein, Bioinformatics ('09, in press)]
Network Dynamics #1: Cellular States How do networks change across different cellular states?  How can this be used to assign function to a protein?
Global topological measures ,[object Object],Degree ( K  ) Path length ( L  ) Clustering coefficient ( C  ) [Barabasi] Interaction and expression networks are  undirected 5 2 1/6
Global topological measures for directed networks In-degree Regulatory and metabolic networks are  directed Out-degree 5 3 TFs Targets
Scale-free networks Hubs  dictate the structure of the network log(Degree) log(Frequency) Power-law distribution [Barabasi]
Hubs tend to be Essential Essential Non- Essential Integrate gene essentiality data with protein interaction network. Perhaps hubs represent vulnerable points? [ Lauffenburger, Barabasi] "hubbiness" [Yu et al., 2003, TIG]
Relationships extends to "Marginal Essentiality" Essential Not important Marginal essentiality measures relative importance of each gene (e.g. in growth-rate and condition-specific essentiality experiments) and scales continuously with "hubbiness" important Very important "hubbiness" [Yu et al., 2003, TIG]
[object Object],[object Object],[object Object],[object Object],Target Genes Transcription Factors Dynamic  Yeast TF network Luscombe et al. Nature 431: 308
Gene expression data for five cellular conditions in yeast Multi-stage  Binary [Brown, Botstein, Davis….] Cellular condition Cell cycle Sporulation Diauxic shift DNA damage Stress response
Backtracking to find active sub-network Active regulatory sub-network ,[object Object],[object Object],[object Object]
Network usage under different conditions static Luscombe et al. Nature 431: 308
Network usage under different conditions cell cycle
Network usage under different conditions sporulation
Network usage under different conditions diauxic shift
Network usage under different conditions DNA damage
Network usage under different conditions stress response
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Network usage under different conditions Cell cycle Sporulation Diauxic shift DNA damage Stress Luscombe et al. Nature 431: 308
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Network usage under different conditions Cell cycle Sporulation Diauxic shift DNA damage Stress
Analysis of condition-specific subnetworks in terms of global topological statistics Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic  shift DNA damage Stress response Outdegree Indegree Pathlength Clustering coefficient Binary Quick, large-scale  turnover of genes Multi-stage Controlled, ticking  over of genes  at different stages
Analysis of condition-specific subnetworks in terms of occurrence of local motifs Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic  shift DNA damage Stress response Single-input module Multi-input module Feed-forward loop Binary Quick, large-scale  turnover of genes Multi-stage Controlled, ticking  over of genes  at different stages
Summary multi-stage conditions binary conditions Cell cycle Sporulation Diauxic shift DNA damage Stress less pronounced Hubs more pronounced longer Path Lengths shorter more TF inter-regulation less complex loops (FFLs) Motifs simpler (SIMs)
Transient Hubs Regulatory hubs Luscombe et al. Nature 431: 308 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Luscombe et al. Nature 431: 308 transitient hubs permanent hubs transient hubs permanent hubs cell cycle sporulation diauxic shift DNA damage stress response all conditions
Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Swi4, Mbp1 Ime1, Ume6 Msn2, Msn4 cell cycle sporulation diauxic shift DNA damage stress response all conditions
Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Unknown functions cell cycle sporulation diauxic shift DNA damage stress response all conditions
Network Dynamics #2: Environments How do molecular networks change across environments?  What pathways are used more ?  Used as a biosensor ?
What is metagenomics?
Comparative Metagenomics Water Soil Do the proportions of pathways represented in these two samples differ? Dinsdale et. al., Nature 2008
Trait-based Biogeography Green et. al., Science 2008 Average Yearly  Rainfall (mm) Leaf span Leaf area Do the proportions of pathways represented in these two samples  CHANGE   as a function of their environments? Long Island  Sound, CT Charles River,  MA
Global Ocean Survey Statistics (GOS) 6.25 GB of data 7.7M Reads 1 million CPU hours  to process Rusch, et al., PLOS Biology 2007
Expressing data as matrices indexed by site, env. var., and pathway usage  Pathway Sequences (Community Function) Environmental  Features [Rusch et. al., (2007) PLOS Biology;  Gianoulis et al., PNAS (in press, 2009]
Simple Relationships: Pairwise Correlations [ Gianoulis et al., PNAS (in press, 2009) ] Pathways
Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a
Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a Metabolic  Pathways Environmental Features Temp Chlorophyll etc Photosynthesis Lipid Metabolism etc
Environmental-Metabolic Space The goal of this technique is to interpret cross-variance matrices We do this by defining a change of basis. [ Gianoulis et al., PNAS (in press, 2009) ] a,b
Circuit Map Environmentally  invariant Environmentally  variant Strength of Pathway co-variation with environment  [ Gianoulis et al., PNAS (in press, 2009) ]
Conclusion #1: energy conversion strategy, temp and depth [ Gianoulis et al., PNAS (in press, 2009) ]
Conclusion #2: Outer Membrane components vary the environment [ Gianoulis et al., PNAS (in press, 2009) ]
Conclusion #3: Covariation of AA biosynthesis and Import Why is their fluctuation in amino acid metabolism? Is there a feature(s) that  underlies those that are  environmentally-variant  as opposed to those which are not? [ Gianoulis et al., PNAS (in press, 2009) ]
Conclusion #4: Cofactor (Metal) Optimization Methionine degradation Polyamine biosynthesis Spermidine/Putrescine transporters Methionine synthesis Cobalamin biosynthesis Cobalt transporters Methionine Salvage IS DEPENDENT-ON Methionine IS NEEDED FOR S-adenosyl Methionine Biosynthesis (synthesize SAM one of the most important methyl donors) RELIES ON Arg/His/Ornithine transporters Methionine salvage, synthesis,  and uptake, transport [ Gianoulis et al., PNAS (in press, 2009) ]
Biosensors: Beyond Canaries in a Coal Mine [ Gianoulis et al., PNAS (in press, 2009) ]
Networks & Variation Which parts of the network vary most in sequence?  Which are under selection, either positive or negative?
METHODOLOGY: MAP SNP AND CNV DATA ONTO ENSEMBL GENES, AND THEN MAP ENSEMBL GENES TO THE KNOWN INTERACTOME * From Nielsen et al.  PLoS Biol.  (2005) and Bustamante et al.  Nature  (2005) Source: PMK ILLUSTRATIVE Hapmap/Perlegen ENSG000XXXX: rsSNP00XXX CNV_XXX DN/DS XXXX Recombination rate  Map to ENSEMBL genes Interactome SNPs ~30000 interactions from HPRD and Y2H screens Database of Genomic Variants Map to proteins in the  interaction network Ensembl Genes ,[object Object],Result CNVs + SDs
ADAPTIVE EVOLUTION CAN BE SEEN ON TWO DIFFERENT LEVELS Intra-species variation Fixed mutations (differences to other species) Single- basepair Structural variation Copy Number Variants Single-Nucleotide Polymorphisms Segmental Duplications Fixed Differences Source: PMK Positive Selection Positive Selection
POSITIVE SELECTION LARGELY TAKES PLACE AT THE NETWORK PERIPHERY Source: Nielsen et al.  PLoS Biol.  (2005), HPRD, and Kim et al. PNAS (2007) High likelihood of positive selection Lower likelihood of positive selection Not under positive selection No data about positive selection Positive selection in the human interactome
CENTRAL PROTEINS ARE LESS LIKELY TO BE UNDER POSITIVE SELECTION ,[object Object],[object Object],[object Object],[object Object],[object Object],Degree vs. Positive Selection Reasoning * With a probability of over 80% to be positively selected as determined by Ka/Ks. Other tests of positive selection (McDonald Kreitmann and LDD) corroborate this result. Source: Nielsen et al.  PLoS Biol.  (2005), Bustamante et al.  Nature  (2005), HPRD, Rual et al.  Nature  (2005), and Kim et al. PNAS (2007) Hubs
CENTRAL NODES ARE LESS LIKELY TO LIE INSIDE OF SDs ,[object Object],[object Object],[object Object],Centrality vs. SD occurrence Reasoning * Specifically, a number of the SDs are likely not fixed, but rather common CNVs in the reference genome Source: Database of genetic variation, HPRD, Rual et al.  Nature  (2005), and Kim et al. PNAS (2007)
BURIED SITES ARE CONSERVED AND MUCH LESS LIKELY TO HARBOR NON-SYNONYMOUS MUTATIONS p<<0.01 Buried sites dN/dS Ratio Exposed sites p<<0.01 Site with  Synonymous Mutations only Sites with Non-synonymous Mutations Average Relative Surface Exposure Source: Kim et al. PNAS (2007) Why do we observer this? Perhaps central hub proteins are involved in more interactions & have more surface buried.
Another explanation: THE NETWORK PERIPHERY CORRESPONDS TO THE CELLULAR PERIPHERY Chromosome Nucleus Cytoplasm Membrane Extracellular Region Betweenness Centrality (x 10 4 ) Degree Centrality Source: Gandhi et al. ( Nature Genetics  2006), Kim et al. PNAS (2007)
IS RELAXED CONSTRAINT OR ADAPTIVE EVOLUTION THE REASON FOR THE PREVALENCE OF BOTH SELECTED GENES AND SDs AT THE NETWORK PERIPHERY? Source: Kim et al. PNAS (2007) Relaxed Constraint Adaptive Evolution ILLUSTRATIVE ,[object Object],[object Object],Inter-Species Variation (Fixed differences) Intra-Species Variation (Polymorphisms) ,[object Object],[object Object],[object Object],[object Object],[object Object]
SOME, BUT NOT ALL OF THE SINGLE-BASEPAIR SELECTION AT THE PERIPHERY IS DUE TO RELAXED CONSTRAINT ,[object Object],[object Object],[object Object],[object Object],Inter vs. Intra-Species Variation in Networks Reasoning * But it’s hard to quantify Source: Kim et al. (2007) PNAS Inter-Species (Fixed differences) Intra-Species (SNPs)  [ Variability ]
Similar Results for Large-scale Genomic Changes (CNVs and SDs) ,[object Object],[object Object],Inter vs. Intra-Species Variation in Networks Reasoning Source: Kim et al. (2007) PNAS Inter-Species (SDs) Intra-Species (CNVs)  [ Variability ]
Conclusions:  Net Intro. + Predicting Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions: Network Dynamics across Cellular States ,[object Object],[object Object],[object Object],[object Object]
Conclusions: Networks Dynamics across Environments ,[object Object],[object Object],[object Object],[object Object]
Conclusions: Connecting Networks & Human Variation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TopNet – an automated web tool [Yu et al., NAR (2004); Yip et al. Bioinfo. (2006);  Similar tools include Cytoscape.org, Idekar, Sander et al] (vers. 2 : &quot; TopNet -like  Yale  Network Analyzer&quot;) ,[object Object],[object Object]
  (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu TopNet.GersteinLab.org Acknowledgements MG MS
Acknowledgements TopNet.GersteinLab.org   (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MS MG
Acknowledgements TopNet.GersteinLab.org   (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MS MG
Acknowledgements TopNet.GersteinLab.org   (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu H Yu P Kim K Yip J Lu S Douglas M Seringhaus Y Xia T Gianoulis J Korbel A Sboner P Patel P Cayting P Bork, J Raes Job opportunities currently for postdocs & students N Luscombe A Paccanaro S Teichmann M Babu MS MG
Extra
DPM: Discriminative Partition Matching Cluster (Partition) Test Environment Metabolism Taurine biosynthesis Heme biosynthesis Asparagine degradation Nitrogen fixation Acylglycerol degradation Asparagine biosynthesis Cysteine Metabolism pval Functional class [ Gianoulis et al., PNAS (in press, 2009) ] InfoStorage & Processing .07 Cellular Process .08 Metabolism 4x10-14
Interactome [From H Yu] protein-DNA interactions protein-protein interactions PROTEOME GENOME Citrate Cycle METABOLISM Protein-small molecule interactions
Networks help us understand biological processes [From H Yu]
More Information on this Talk ,[object Object],[object Object],[object Object],[object Object]

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Cornell Pbsb 20090126 Nets

  • 1. Understanding Protein Function on a Genome-scale through the Analysis of Molecular Networks Mark B Gerstein Yale Slides at Lectures.GersteinLab.org (See Last Slide for References & More Info.) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu
  • 2.
  • 3. Traditional single molecule way to integrate evidence & describe function Descriptive Name: Elongation Factor 2 Summary sentence describing function: This protein promotes the GTP-dependent translocation of the nascent protein chain from the A-site to the P-site of the ribosome. EF2_YEAST Lots of references to papers
  • 4.
  • 5.
  • 6. Hierarchies & DAGs of controlled-vocab terms but still have issues... [Seringhaus & Gerstein, Am. Sci. '08] GO (Ashburner et al.) MIPS (Mewes et al.)
  • 7.
  • 8. Networks (Old & New) [Seringhaus & Gerstein, Am. Sci. '08] Classical KEGG pathway Same Genes in High-throughput Network
  • 9. Networks occupy a midway point in terms of level of understanding 1D: Complete Genetic Partslist ~2D: Bio-molecular Network Wiring Diagram 3D: Detailed structural understanding of cellular machinery [Jeong et al. Nature, 41:411] [Fleischmann et al., Science, 269 :496]
  • 10. Networks as a universal language Disease Spread [Krebs] Protein Interactions [Barabasi] Social Network Food Web Neural Network [Cajal] Electronic Circuit Internet [Burch & Cheswick]
  • 11. Networks as a Central Theme in Systems Biology Reductionist Approach Integrative Approach [Adapted from H Yu] Systems Biology
  • 12. Interactome networks Network pathology & pharmacology [Adapted from H Yu] Breast Cancer Parkinson’s Disease Alzheimer’s Disease Multiple Sclerosis
  • 13. Using the position in networks to describe function [NY Times, 2-Oct-05, 9-Dec-08]
  • 14. Types of Networks Interaction networks [Horak, et al, Genes & Development, 16:3017-3033] [DeRisi, Iyer, and Brown, Science, 278:680-686] [Jeong et al, Nature, 41:411] Regulatory networks Metabolic networks Nodes: proteins or genes Edges: interactions
  • 15. Combining networks forms an ideal way of integrating diverse information Metabolic pathway Transcriptional regulatory network Physical protein-protein Interaction Co-expression Relationship Part of the TCA cycle Genetic interaction (synthetic lethal) Signaling pathways
  • 16.
  • 17. Predicting Networks How do we construct large molecular networks? From extrapolating correlations between functional genomics data with fairly small sets of known interactions, making best use of the known training data.
  • 18.
  • 19.
  • 20.
  • 21. Training sets Known interactions Known non-interactions Unknown 1 2 4 3 1 2 3 4 1 0 1 ? 1 2 1 ? 0 ? 3 ? 0 ? ? 4 1 ? ? ?
  • 22.
  • 23.
  • 24. Data integration & Similarity Matrix 1 2 4 3 1 2 4 3 1 2 4 3 1 2 4 3 1 2 3 4 1 1.00 0.57 0.55 0.40 2 0.57 1.00 0.66 0.89 3 0.55 0.66 1.00 0.79 4 0.40 0.89 0.79 1.00
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37. Complementarity of the two methods [Yip and Gerstein, Bioinformatics ('09, in press)]
  • 38. Network Dynamics #1: Cellular States How do networks change across different cellular states? How can this be used to assign function to a protein?
  • 39.
  • 40. Global topological measures for directed networks In-degree Regulatory and metabolic networks are directed Out-degree 5 3 TFs Targets
  • 41. Scale-free networks Hubs dictate the structure of the network log(Degree) log(Frequency) Power-law distribution [Barabasi]
  • 42. Hubs tend to be Essential Essential Non- Essential Integrate gene essentiality data with protein interaction network. Perhaps hubs represent vulnerable points? [ Lauffenburger, Barabasi] &quot;hubbiness&quot; [Yu et al., 2003, TIG]
  • 43. Relationships extends to &quot;Marginal Essentiality&quot; Essential Not important Marginal essentiality measures relative importance of each gene (e.g. in growth-rate and condition-specific essentiality experiments) and scales continuously with &quot;hubbiness&quot; important Very important &quot;hubbiness&quot; [Yu et al., 2003, TIG]
  • 44.
  • 45. Gene expression data for five cellular conditions in yeast Multi-stage Binary [Brown, Botstein, Davis….] Cellular condition Cell cycle Sporulation Diauxic shift DNA damage Stress response
  • 46.
  • 47. Network usage under different conditions static Luscombe et al. Nature 431: 308
  • 48. Network usage under different conditions cell cycle
  • 49. Network usage under different conditions sporulation
  • 50. Network usage under different conditions diauxic shift
  • 51. Network usage under different conditions DNA damage
  • 52. Network usage under different conditions stress response
  • 53.
  • 54.
  • 55. Analysis of condition-specific subnetworks in terms of global topological statistics Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic shift DNA damage Stress response Outdegree Indegree Pathlength Clustering coefficient Binary Quick, large-scale turnover of genes Multi-stage Controlled, ticking over of genes at different stages
  • 56. Analysis of condition-specific subnetworks in terms of occurrence of local motifs Luscombe et al. Nature 431: 308 Sporulation Cell cycle Diauxic shift DNA damage Stress response Single-input module Multi-input module Feed-forward loop Binary Quick, large-scale turnover of genes Multi-stage Controlled, ticking over of genes at different stages
  • 57. Summary multi-stage conditions binary conditions Cell cycle Sporulation Diauxic shift DNA damage Stress less pronounced Hubs more pronounced longer Path Lengths shorter more TF inter-regulation less complex loops (FFLs) Motifs simpler (SIMs)
  • 58.
  • 59.
  • 60. Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Swi4, Mbp1 Ime1, Ume6 Msn2, Msn4 cell cycle sporulation diauxic shift DNA damage stress response all conditions
  • 61. Luscombe et al. Nature 431: 308 transitient hubs permanent hubs Unknown functions cell cycle sporulation diauxic shift DNA damage stress response all conditions
  • 62. Network Dynamics #2: Environments How do molecular networks change across environments? What pathways are used more ? Used as a biosensor ?
  • 64. Comparative Metagenomics Water Soil Do the proportions of pathways represented in these two samples differ? Dinsdale et. al., Nature 2008
  • 65. Trait-based Biogeography Green et. al., Science 2008 Average Yearly Rainfall (mm) Leaf span Leaf area Do the proportions of pathways represented in these two samples CHANGE as a function of their environments? Long Island Sound, CT Charles River, MA
  • 66. Global Ocean Survey Statistics (GOS) 6.25 GB of data 7.7M Reads 1 million CPU hours to process Rusch, et al., PLOS Biology 2007
  • 67. Expressing data as matrices indexed by site, env. var., and pathway usage Pathway Sequences (Community Function) Environmental Features [Rusch et. al., (2007) PLOS Biology; Gianoulis et al., PNAS (in press, 2009]
  • 68. Simple Relationships: Pairwise Correlations [ Gianoulis et al., PNAS (in press, 2009) ] Pathways
  • 69. Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a
  • 70. Canonical Correlation Analysis: Simultaneous weighting [ Gianoulis et al., PNAS (in press, 2009) ] + b’ + c’ GPI = a’ + b + c UPI = a Metabolic Pathways Environmental Features Temp Chlorophyll etc Photosynthesis Lipid Metabolism etc
  • 71. Environmental-Metabolic Space The goal of this technique is to interpret cross-variance matrices We do this by defining a change of basis. [ Gianoulis et al., PNAS (in press, 2009) ] a,b
  • 72. Circuit Map Environmentally invariant Environmentally variant Strength of Pathway co-variation with environment [ Gianoulis et al., PNAS (in press, 2009) ]
  • 73. Conclusion #1: energy conversion strategy, temp and depth [ Gianoulis et al., PNAS (in press, 2009) ]
  • 74. Conclusion #2: Outer Membrane components vary the environment [ Gianoulis et al., PNAS (in press, 2009) ]
  • 75. Conclusion #3: Covariation of AA biosynthesis and Import Why is their fluctuation in amino acid metabolism? Is there a feature(s) that underlies those that are environmentally-variant as opposed to those which are not? [ Gianoulis et al., PNAS (in press, 2009) ]
  • 76. Conclusion #4: Cofactor (Metal) Optimization Methionine degradation Polyamine biosynthesis Spermidine/Putrescine transporters Methionine synthesis Cobalamin biosynthesis Cobalt transporters Methionine Salvage IS DEPENDENT-ON Methionine IS NEEDED FOR S-adenosyl Methionine Biosynthesis (synthesize SAM one of the most important methyl donors) RELIES ON Arg/His/Ornithine transporters Methionine salvage, synthesis, and uptake, transport [ Gianoulis et al., PNAS (in press, 2009) ]
  • 77. Biosensors: Beyond Canaries in a Coal Mine [ Gianoulis et al., PNAS (in press, 2009) ]
  • 78. Networks & Variation Which parts of the network vary most in sequence? Which are under selection, either positive or negative?
  • 79.
  • 80. ADAPTIVE EVOLUTION CAN BE SEEN ON TWO DIFFERENT LEVELS Intra-species variation Fixed mutations (differences to other species) Single- basepair Structural variation Copy Number Variants Single-Nucleotide Polymorphisms Segmental Duplications Fixed Differences Source: PMK Positive Selection Positive Selection
  • 81. POSITIVE SELECTION LARGELY TAKES PLACE AT THE NETWORK PERIPHERY Source: Nielsen et al. PLoS Biol. (2005), HPRD, and Kim et al. PNAS (2007) High likelihood of positive selection Lower likelihood of positive selection Not under positive selection No data about positive selection Positive selection in the human interactome
  • 82.
  • 83.
  • 84. BURIED SITES ARE CONSERVED AND MUCH LESS LIKELY TO HARBOR NON-SYNONYMOUS MUTATIONS p<<0.01 Buried sites dN/dS Ratio Exposed sites p<<0.01 Site with Synonymous Mutations only Sites with Non-synonymous Mutations Average Relative Surface Exposure Source: Kim et al. PNAS (2007) Why do we observer this? Perhaps central hub proteins are involved in more interactions & have more surface buried.
  • 85. Another explanation: THE NETWORK PERIPHERY CORRESPONDS TO THE CELLULAR PERIPHERY Chromosome Nucleus Cytoplasm Membrane Extracellular Region Betweenness Centrality (x 10 4 ) Degree Centrality Source: Gandhi et al. ( Nature Genetics 2006), Kim et al. PNAS (2007)
  • 86.
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94. (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu TopNet.GersteinLab.org Acknowledgements MG MS
  • 95. Acknowledgements TopNet.GersteinLab.org (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MS MG
  • 96. Acknowledgements TopNet.GersteinLab.org (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MS MG
  • 97. Acknowledgements TopNet.GersteinLab.org (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu H Yu P Kim K Yip J Lu S Douglas M Seringhaus Y Xia T Gianoulis J Korbel A Sboner P Patel P Cayting P Bork, J Raes Job opportunities currently for postdocs & students N Luscombe A Paccanaro S Teichmann M Babu MS MG
  • 98. Extra
  • 99. DPM: Discriminative Partition Matching Cluster (Partition) Test Environment Metabolism Taurine biosynthesis Heme biosynthesis Asparagine degradation Nitrogen fixation Acylglycerol degradation Asparagine biosynthesis Cysteine Metabolism pval Functional class [ Gianoulis et al., PNAS (in press, 2009) ] InfoStorage & Processing .07 Cellular Process .08 Metabolism 4x10-14
  • 100. Interactome [From H Yu] protein-DNA interactions protein-protein interactions PROTEOME GENOME Citrate Cycle METABOLISM Protein-small molecule interactions
  • 101. Networks help us understand biological processes [From H Yu]
  • 102.