Cytoscape An open-source software platform for the exploration of molecular interaction networks

Transcription

1 Cytoscape An open-source software platform for the exploration of molecular interaction networks Systems Biology Group UP Biologie Systémique Institut Pasteur, Paris

2 Overview 1. Molecular interaction networks 2. The Cytoscape platform 3. Active modules

3 Biology poses difficulties for approaches from physics and engineering What are the effects of mutations on the system? How do multiple mutations modify interactions with the environment? How are molecular processes controlled? Courtesy L. Hood

4 Biological and engineering models Lazebnik, Cancer Cell, Sep. 2002

5 Molecular interaction networks

6 Cartoons may not be enough Taken together, these data suggest that EBF & E2A may be less important than Pax-5 for regulating low-level level mb-1 transcription at later stages of development. Mol. Cell Biol. Dec 02, p.8850 and drawn cartoons These stories might be too qualitative too hard to integrate

7 Cartoons are hard to combine The stories are designed around specific questions They are not written against a single conceptual scaffold We may not be able to integrate cartoons into coherent models Courtesy H. Bolouri

8 Uses for the study of biological networks and systems Better characterize the function of single genes Help structure, represent and interpret experimental data on interactions and states Integrate different types of experimental data Relate mechanisms to states Build a detailed understanding of cellular processes Allow prediction of cellular state observables at different levels of detail Allow intervention with predictable & measurable outcomes Guide experiments by providing testable hypotheses Compare processes within and across organisms

9 The source of the network parts list DNA sequencer DNA mrna Proteins Pathways Networks Cells Tissues Organs Individuals Populations Ecosystems DNA sequences

10 Sources of network state information DNA mrna Proteins Pathways Networks Cells Tissues Organs Individuals Populations Ecosystems DNA microarray

11 Sources of network state information Mass spectrometer DNA mrna Proteins Pathways Networks Cells Tissues Organs Individuals Populations Ecosystems Mass spectrum

12 Sources of network interaction information: The Two-Hybrid System Two hybrid proteins are generated with transcription factor domains Both fusions are expressed in a yeast cell that carries a reporter gene whose expression is under the control of binding sites for the DNAbinding domain Bait Protein Binding Domain Activation Domain Prey Protein Reporter Gene

13 Sources of network interaction information: The Two-Hybrid System Interaction of bait and prey proteins localizes the activation domain to the reporter gene, thus activating transcription. Since the reporter gene typically codes for a survival factor, yeast colonies will grow only when an interaction occurs. Activation Domain Prey Protein Bait Protein Binding Domain Reporter Gene

14 Sources of network information: ChIP-chip CHromatin ImmunoPreciptation-Chip (ChIP-Chip) Analysis (Ren et al., Science, 2000) From Richard Young s Website

15

16 Metabolic networks are fairly detailed Stoichiometric matrix network topology with stoichiometry of biochemical reactions Glucose + ATP Glucokinase Glucose-6-Phosphate + ADP Glucokinase Glucose -1 ATP -1 G-6-P +1 ADP +1 Mass balance S v = 0 Subspace of R n Thermodynamic v i > 0 Convex cone Capacity v i < v max Bounded convex cone

17 Comparative assessment of large-scale data sets of proteinprotein interactions. Von Mering, C. Nature 2002 Among the [protein-protein] interactions proposed by high-throughput methods will be many false positives. In fact, we estimate that more than half of all current high-throughput data are spurious.

18 Gene regulation can be complex Yuh, Bolouri, Davidson, Science, 1998

19 High- and low-level modeling may be combined Ideker and Lauffenburger, Trends in Biotechnology 2003

20 High- and low-level modeling Ideker and Lauffenburger, Trends in Biotechnology 2003

21 Cytoscape Network Visualization and Analysis Courtesy M. Smoot

22 Cytoscape Overview Rich network visualizations Powerful data mapping Handles large networks Supports many standards Large community Free (open-source)! 22

23 Network Data Import SIF (Simple Interaction Format) GML (Graph Markup Language) XGMML (extensible Graph Markup and Modeling Language) BioPax (Biological Pathway Data) PSI-MI 1 & 2.5 (Protein Standards Initiative) SBML Level 2 (Systems Biology Markup Language) 23

24 Formatted Text and Excel Files 24

25 Network Attribute Management 25

26 Data Integration 1. Network Data YDR382W pp YDL130W YDR382W pp YFL039C YFL039C pp YCL040W YFL039C pp YHR179W VizMapper 2. Attribute Data ExpressionValue YCL040W = YDL130W = YDR382W = YFL039C = YHR179W =

27 VizMapper Map network state data onto visual attributes. Attributes for nodes and edges. Very Flexible. 27

28 Expression Data Node Color 28

29 Layout Algorithms 29

30 Network Editor 30

31 Filters 31

32 Linkout Nodes and Edges act as hyperlinks to external databases. User configurable URLs. 32

33 Large Networks 19,462 Nodes 31,130 Edges Only half of what's possible! 33

34 Other Features Manual Layout manipulation tools align, scale, rotate Manually override visual styles Undo Can undo most modifications to graphs Publication Quality Graphics Export PDF, SVG, PS 34

35 Cytoscape is Extensible Cytoscape is open-source. We provide a plug-in interface that allows anyone to write and distribute their own extensions to Cytoscape. Plug-ins represent the primary analysis mechanism in Cytoscape. Plug-ins are distributed from a central database and can installed while running. 35

36 Plugin Examples BiNGO (Analysis of GO categories found in network) GenePro (Protein-Protein interaction cluster visualization) jactivemodules (Search for significant networks) NetworkAnalyzer (Statistical analysis of networks) Agilent Literature Search (Network creation) CyGoose (Gaggle communication) See for many more 36

37 Running Cytoscape Cytoscape is licensed under the LGPL and is therefore freely available to everyone. Cytoscape is written in Java and therefore runs on Windows, Mac, and Linux. Cytoscape can be run locally or using Webstart. 37

38 Cytoscape applications Cytoscape facilitates: Network Visualization Network Analysis Data Integration A framework for new types of analysis 38

39 Cytoscape Consortium UC San Diego (Trey Ideker) Institute for Systems Biology (Leroy Hood/Ilya Shmulevich) Memorial Sloan-Kettering Cancer Center (Chris Sander) University of Toronto (Gary Bader) Agilent Technologies (Annette Adler) Unilever (Guy Warner) UC San Francisco (Bruce Conklin) Institut Pasteur () NIGMS/NIH GM

40 Getting started with Cytoscape Tutorials on Cytoscape.org Nature Protocols paper QuickTime and a decompressor are needed to see this picture. QuickTime and a decompressor are needed to see this picture. Systemsbiology.fr

41 Active Modules

42 Protein interaction networks

43 Protein-protein interactions in yeast Questions Is there any correlation between protein interactions and other attributes of proteins? Is that correlation significant, i.e., would it not easily occur in random data?

44 Functionally related proteins occur as clusters of interacting proteins

45 Protein interactions contain information about cellular roles Simple prediction algorithm for the cellular role of a protein 1) Rank known cellular roles among the interactors from most frequent to least frequent. 2) Take the first three (or less) roles as predictions. Accuracy on 1,393 out of 2,039 proteins: 72% (6 out of 8) on 100 scrambled networks: 12% (1 out of 8).

46 Protein interactions provide context information RNA splicing Mayer & Hieter, Nature Biotechnology 2000

47 Modular structure of cellular networks Hartwell et al., Nature 1999

48 The cell as an information processor Hartwell et al., Nature 1999

49 Advantage of modules Theoretical There are 2^n different boolean functions on n variables Practical implication There are fewer components and fewer experiments to perform x 1 x 2 x 3 f

50 Molecular Interaction Network

51 The system notion

52 Approach 1. Use interaction data: The system components have to interact with each other 2. Use state data: System components have to change synchronously

53 Approach Summary 1. Interaction network between genes/proteins 2. Differential Gene/Protein Abundances/Activities Experiments Conditions -> gal1d gal2d gal3d gal4d gal5d gal6d gal7d ga Genes COX NDT PRS UPF OPI YGR145W YGL041C CRM HIS CIT KHS YBR026C YMR244W YMR317W YAR047C DAL YDL177C YLR338W YGR073C YGR146C

54 Comparison to clustering 1. Connectivity by scaffold of protein interactions Direct causal explanations and testable hypotheses 2. Significant change observed under certain experimental conditions Module need not be active under all experimental conditions

55 Galactose induction pathway Ideker et al. Science 292: 929 (2001)

56 What are the underlying regulatory interactions responsible for the observed changes in gene expression? Prot. prot. interactions BIND ~ 6300 proteins, interactions in yeast RNA-expression data 20 perturbations of the galactose utilization pathway Prot. DNA interactions Transfac/ChIP data ~10,000 interactions for yeast INTEGRATED MOLECULAR INTERACTION NETWORK Protein expression data abundances, modifications, translation states Small mol. interactions Metabolites, drugs, and hormones: KEGG, enzymes, etc. Metabolic profiles Abundances may soon be avail. on a global scale This technique is extensible to a variety of data types.

57 The galactose pathway in our network representation Expression change (log10) protein DNA We consider only the significance of change, not its direction. protein protein Ideker et al. Science 292: 929 (2001)

58 Module A mathematical definition

59 A scoring system for regulatory activity Assign significance to each gene expression change and express as a z-score The z-score of an entire subnetwork is the normalized sum of scores of its nodes Ideker, Thorsson, Siegel, and Hood, J. Comp. Bio. 7: 805 (2000) A B C D Perturbations /conditions

60 The p(1.0)=0.159 z(0.159)=1.0 p-value z-score

61 Combining z-scores under one condition A B C D = 1

62 Scoring over multiple perturbations/conditions A(1) Perturbations /conditions A(2) A(3) A(4)

63 Scoring over multiple conditions Rank adjustment What is the probability that, out of m z-scores, the first j ones are larger than A(j)? Idea: Compute the probability that j or more z-scores are larger than A(j): where

64 Scoring over multiple perturbations/conditions Final Score Perturbations /conditions

65 Different overlapping condition sets Each condition may appear several times, or not at all, depending on how well it is (a) significant and (b) explained by the interaction network. Each subnetwork is active for a subset of conditions Running the algorithm again on the high-scoring, 340- gene subnetwork reveals further structure

66 Pathways in Rosetta s compendium (300 conditions)

67 Getting started with Cytoscape Tutorials on Cytoscape.org Nature Protocols paper QuickTime and a decompressor are needed to see this picture. QuickTime and a decompressor are needed to see this picture. Systemsbiology.fr

68 THANK YOU FOR YOUR ATTENTION

69 Finding good modules

70 Finding good modules in a large network is hard Once specified, we can easily score a particular pathway. But how to identify the highest-scoring pathways in a full molecular interaction network of thousands of nodes and interactions? This problem is NP-complete, We use a customized version of a general-purpose algorithm to detect high-scoring pathways from the data. Use a method based on simulated annealing.

71 Computational complexity 6,000 genes form up to 2 6,000 possible gene sets 300 conditions have subsets 2 180,000 > 10 50,000 combinations to search Finding the highest-scoring gene set is NP-hard, even for a single condition

72 NP-hardness NP-hardness is a property of computational problems It implies any algorithm that solves the problem runs at least as long as thousands of other well-known problems Efficient algorithms for NP-hard problems are unknown (and probably don t exist) Thus, need to look for approximation or heuristic algorithms

73 20 GAL conditions vs. the entire interaction network

74 Several subnetworks emerge

75 Detail of subnetwork 1b Galactose metabolism Our method is only concerned with the significance of change, not its direction. Gal4 doesn t show dramatic expression change, but it is included because it connects and explains the other genes differential expression.

76 Galactose induction pathway Ideker et al. Science 292: 929 (2001)

77 SUMMARY Method for explaining gene expression profiles with molecular interactions found in the public databases. Results in testable hypotheses for the signaling and regulatory pathways behind observed gene expression changes.

78 Features of this approach Tries to define clusters of genes that show similar concerted reactions to perturbations Incorporates many data types Robust against noise, false positive interactions Many, and experiment-specific networks identified Interpretive framework offers testable hypotheses

79 The system notion