1. Introduction to Systems Biology

2. Overview of the day Background & Introduction Network analysis methods Case studies Exercises

3. Why Systems Biology? …and why now?

4. Timeline of discovery 1862 Louis Pasteur: Microorganisms responsible for contamination, heating kills microorganisms van Leeuwenhoek: described single celled organisms 1676 1866 Gregor Mendel: Phenotype determined by inheritable units 1735 Carl Linnaeus: Hierarchical classification of species 1859 Charles Darwin: “The Origin of Species” 1944 Avery, MacLeod, McCarty: DNA is the genetic material 1953 James Watson Francis Crick: solve structure of DNA Frederick Sanger: Complete sequence of insulin 1955

5. Frederick Sanger In 1975, he developed the chain termination method of DNA sequencing, also known as the Dideoxy termination method or the Sanger method. Two years later he used his technique to successfully sequence the genome of the Phage Φ-X174; the first fully sequenced genome. This earned him a Nobel Prize in Chemistry (1980) (his second) –Sanger earned his first Nobel prize in Chemistry (1958) for determining the complete amino acid sequence of insulin in 1955. Concluded that insulin had a precise amino acid sequence.

6. The genomic era Human genome sequence “completed”, Feb 2001

7. PubMed abstracts indicate a recent interest in Systems Biology Human genome completed

8. High-throughput sequencing Clone-by-Clone –Slower, easier to assemble (more accurate??) –Expensive Shotgun approach –Faster, cheaper, difficult to assemble 454 approach –Extremely fast, short reads (~100bp) –Very cheap –Gets us closer to the $1000 genome Sequencing by hybridization (microarrays) –Usually requires a complete reference genome

9. Functional genomics Study of Genomes is called “Genomics” Genomics led to Functional Genomics which aims to characterize and determine the function of biomolecules (mainly proteins), often by the use of high-throughput technologies. Today, people talk about: –Genomics –Transcriptomics –Proteomics –Metabolomics –[Anything]omics

10. DNA microarray overview Microarrays are composed of short DNA oligomers attached to an inert substrate –glass slide, nylon membrane (historically) Typically contain a grid of 10 5 -10 6 features (spots) each with a different DNA molecule Fluorescently-labeled DNA or RNA hybridizes to complementary probes Hybridized array is scanned with a laser to produce a signal for each spot cDNA arrays: Spotted technology (Stanford) Oligonucleotide arrays: Affymetrix Illumina NimbleGen Agilent

11. Tiling microarrays Huber W, et al., Bioinformatics 2006

12. Functional genomics using gene knockout libraries for yeast similar RNAi libraries in other systems Replacement of yeast ORFs with kanMX gene flanked by unique oligo barcodes- “Yeast Deletion Project Consortium”

13. Systematic phenotyping yfg1  yfg2  yfg3  CTAACTCTCGCGCATCATAAT Barcode (UPTAG): Deletion Strain: Growth 6hrs in minimal media (how many doublings?) Rich media … Harvest and label genomic DNA

14. Systematic phenotyping with a barcode array ( Ron Davis and others) These oligo barcodes are also spotted on a DNA microarray Growth time in minimal media: –Red: 0 hours –Green: 6 hours

15. High-throughput applications of microarrays Gene expression De novo DNA sequencing (short) DNA re-sequencing (relative to reference) SNP analysis Competitive growth assays chIP-chip (interaction data) Array CGH Whole genome tiling arrays

16. Mass spectrometry Peptide identification Relative peptide levels Protein-protein interactions (complexes) Many many technologies

17. MudPIT (Multidimensional Protein Identification Technology) MudPIT describes the process of digesting, separating, and identifying the components of samples consisting of thousands of proteins. Separates peptides by 2D liquid chromatography (cation-exchange followed by reversed phase liquid chromotography) LC interfaced directly with the ion source (microelectrospray) of a mass spectrometer John Yates lab http://fields.scripps.edu/mudpit/index.html

18. Isotope coded affinity tags (ICAT) Biotin tag Linker (d0 or d8) Thiol specific reactive group ICAT Reagents : Heavy reagent: d8-ICAT (X=deuterium) Normal reagent: d0-ICAT (X=hydrogen) S N N O N O O ON I O O X X X XXX X X Mass spec based method for measuring relative protein abundances between two samples Ruedi Aebersold http://www.imsb.ethz.ch/researchgroup/aebersold

19. Combine and proteolyze ) (trypsin) Affinity separation (avidin) ICAT- labeled cysteines 550560570580 m/z 0 100 200400600800 m/z 0 100 NH 2 -EACDPLR- COOH Light Heavy Mixture 2 Mixture 1 Protein quantification & identification via ICAT strategy Quantitation ICAT Flash animation: http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/ICAT/ICAT.html

20. Example Yeast grown in ethanol vs galactose media were monitored with ICAT Adh1 vs. Adh2 ratios are shown below…

21. Comparing mRNA levels to protein levels

22. Protein-protein interaction data Physical Interactions –Yeast two hybrid screens –Affinity purification (mass spec) –Peptide arrays –Protein-DNA by chIP-chip Other measures of ‘association’ –Genetic interactions (double deletion mutants) –Genomic context (STRING)

23. Yeast two-hybrid method Y2H assays interactions in vivo. Uses property that transcription factors generally have separable transcriptional activation (AD) and DNA binding (DBD) domains. A functional transcription factor can be created if a separately expressed AD can be made to interact with a DBD. A protein ‘bait’ B is fused to a DBD and screened against a library of protein “preys”, each fused to a AD.

24. Issues with Y2H Strengths –High sensitivity (transient & permanent PPIs) –Takes place in vivo –Independent of endogenous expression Weaknesses: False positive interactions –Auto-activation –‘sticky’ prey –Detects “possible interactions” that may not take place under real physiological conditions –May identify indirect interactions (A-C-B) Weaknesses: False negatives interactions –Similar studies often reveal very different sets of interacting proteins (i.e. False negatives) –May miss PPIs that require other factors to be present (e.g. ligands, proteins, PTMs)

25. Protein-DNA interactions: ChIP-chip Simon et al., Cell 2001 Lee et al., Science 2002

26. Mapping transcription factor binding sites Harbison C., Gordon B., et al. Nature 2004

27. Dynamic role of transcription factors Harbison C., Gordon B., et al. Nature 2004

28. Exercise: Y2H Construct a protein-protein interaction network for proteins A,B,C,D

29. Systems biology and emerging properties

30. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002

31. Building models from parts lists

32. Protein-DNA interactions Gene levels (up/down) Protein-protein interactions Protein levels (present/absent) Biochemical reactions Biochemical levels ▲ Chromatin IP ▼ DNA microarray ▲ Protein coIP ▼ Mass spectrometry ▲none Metabolic flux ▼ measurements

33. Mathematical abstraction of biochemistry

34. Metabolic models

35. “Genome scale” metabolic models Genes708 Metabolites584 –Cytosolic559 –Mitochondrial164 –Extracellular121 Reactions1175 –Cytosolic702 –Mitochondrial124 –Exchange fluxes349 Forster et al. Genome Research 2003.

36. One framework for Systems Biology 1.The components. Discover all of the genes in the genome and the subset of genes, proteins, and other small molecules constituting the pathway of interest. If possible, define an initial model of the molecular interactions governing pathway function (how?). 2.Pathway perturbation. Perturb each pathway component through a series of genetic or environmental manipulations. Detect and quantify the corresponding global cellular response to each perturbation.

37. One framework for Systems Biology 3.Model Reconciliation. Integrate the observed mRNA and protein responses with the current, pathway- specific model and with the global network of protein- protein, protein-DNA, and other known physical interactions. 4.Model verification/expansion. Formulate new hypotheses to explain observations not predicted by the model. Design additional perturbation experiments to test these and iteratively repeat steps (2), (3), and (4).

38. From model to experiment and back again

39. Systems biology paradigm Aebersold R, Mann M., Nature, 2003.

40. Continuum of modeling approaches Top-downBottom-up

41. Need computational tools able to distill pathways of interest from large molecular interaction databases (top-down) Data integration and statistical mining

42. List of genes implicated in an experiment What do we make of such a result? Jelinsky S & Samson LD, Proc. Natl. Acad. Sci. USA Vol. 96, pp. 1486–1491,1999

43. Types of information to integrate Data that determine the network (nodes and edges) –protein-protein –protein-DNA, etc… Data that determine the state of the system –mRNA expression data –Protein modifications –Protein levels –Growth phenotype –Dynamics over time

44. Mapping the phenotypic data to the network Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12. Systematic phenotyping of 1615 gene knockout strains in yeast Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) Screening against a network of 12,232 protein interactions

45. Mapping the phenotypic data to the network Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

46. Mapping the phenotypic data to the network Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

47. Network models can be predictive Green nodes represent proteins identified as being required for MMS resistance; gray nodes were not tested as part of the 1615 strains used in this study; blue lines represent protein-protein interactions. The untested gene deletion strains (ylr423c, hda1, and hpr5) were subsequently tested for MMS sensitivity; all were found to be sensitive (bottom). Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

48. Summary Systems biology can be either top-down or bottom-up We are now in the post genomic era (don’t ignore that) Systematic measurements of all transcripts, proteins, and protein interactions enable top- down modeling Metabolic models, built bottom-up, are being refined with genomic information Data – Model – Predictions – Data: cycle as a Systems Biology theme

49. IntAct IntAct is funded by the European Commission H. Hermjakob, L. Montecchi-Palazzi, C. Lewington, S. Mudali, S. Kerrien, S. Orchard, M. Vingron, B. Roechert, P. Roepstorff, A. Valencia, H. Margalit, J. Armstrong, A. Bairoch, G. Cesareni, D. Sherman, R. Apweiler. IntAct - an open source molecular interaction database. Nucl. Acids. Res. 2004 32: D452-D455

50. IntAct statistics http://www.ebi.ac.uk/intact/statisticView

51. MINT Zanzoni A., Montecchi-Palazzi L., Quondam M., Ausiello G., Helmer-Citterich M. and Cesareni G. MINT: a Molecular INTeraction database. (2002) FEBS Letters, 513(1);135-140.

52. Metabolic network databases KEGG, Kyoto Encyclopedia of Genes and Genomes –Metabolic pathway database –Much of it based on E. coli Reactome –Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium

53. Reference Pathway

54. Reactome Curated resource of core pathways and reactions in human biology Database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross-referenced with other sequence databases (NCBI, Ensembl, etc)

55. www.reactome.org

56. Reactome example

57. Relevance networks STRING: a database of predicted functional associations between proteins –Per Bork, EMBL Heidelberg Probabilistic gene network, Functional or relevence network – Edward Marcotte, University of Texas Literature networks (there are many methods…)

58. Probabilistic functional network Each experiment is evaluated for its ability to reconstruct known gene pathways and systems by measuring the likelihood that pairs of genes are functionally linked conditioned on the evidence Bayesian statistics, log likelihood scores (LLS) Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science. 2004 Nov 26;306(5701):1555-8

59. Probabilistic functional network where P(L|E) and ~P(L|E) are the frequencies of linkages (L) observed in the given experiment (E) between annotated genes operating in the same pathway and in different pathways, respectively, whereas P(L) and ~P(L) represent the prior expectations Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science. 2004 Nov 26;306(5701):1555-8

60. “A probabilistic functional network of yeast genes” Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science. 2004 Nov 26;306(5701):1555-8

61. “A probabilistic functional network of yeast genes” Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science. 2004 Nov 26;306(5701):1555-8

62. STRING example

63. STRING network view Static Interactive

64. Ontologies MIPS Functional Catalogue “Funcat” –http://mips.gsf.de/proj/funcatDB/http://mips.gsf.de/proj/funcatDB/ The Gene Ontology (GO) –http://www.geneontology.org/http://www.geneontology.org/ –The Gene Ontology Consortium

65. GO is An effort to address the need for consistent descriptions of gene products in different databases via structured controlled vocabularies (ontologies) “Open source” Has three separate ontologies: –Molecular Function –Biological Process –Cellular Component Hierarchical (in nature)

66. GO is NOT Components that are unique to mutants or diseases Attributes of sequence such as introns or exons Protein domains or structural features Protein-protein interactions Environment, evolution and expression Histological features above the level of cellular components

67. GO Ontologies are structured as directed acyclic graphs

68. Fisher’s exact test The hypergeometric distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement. A set of N genes in which L are labeled FunctionA. The hypergeometric distribution describes the probability that in a sample of n distinctive genes drawn from this set exactly k genes are FunctionA.

69. Fisher’s exact test DrawnNot drawnTotal LabeledkL-kL Not labledn-kN+k-n-LN-L TotalnN-nN