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. 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

9. 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

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

11. 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”

12. 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

13. 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

14. Mass spectrometry Peptide identification Relative peptide levels Protein-protein interactions (complexes) Post-translational modifications Many many technologies

15. 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

16. 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

17. 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

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

19. Comparing mRNA levels to protein levels

20. 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)

21. 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.

22. 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)

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

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

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

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

27. Systems biology and emerging properties

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

29. Building models from parts lists

30. 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

31. Mathematical abstraction of biochemistry

32. Metabolic models

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

34. 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.

35. 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).

36. From model to experiment and back again

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

38. Continuum of modeling approaches Top-downBottom-up

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

40. 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

41. 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

42. 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

43. 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.

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.

45. 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.

46. 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