From to Systems Biology Outline and learning objectives Omics science provides global analysis tools to study entire systems How to obtain omics - What can we learn Limitations Integration of omics - In-class practice: Omics- visualization Integration of omics - information Whole- sequencing Microarrays 2D-electrophoresis, mass spectrometry Joyce Palsson, 2006 Joyce Palsson, 2006 (indirectly) tells about RNA-transcript abundances aims to detect and quantify a system s entire protein content! primary genomic readout Strengths: - very good -wide coverage - variety of commercial products Drawback: No protein-level info!! -> RNA degradation -> Post-translational modifications => validation by e.g. RT-PCR Strengths: -> info about post-translational modifications -> high throughput possible due to increasing quality and cycle speed of mass spec instrumentation - coverage dependent on sample, preparation separation method - bias towards most highly abundant proteins
and Lipidomics GC-MS NMR Glycan arrays, Glyco-gene chips, mass spec / NMR of carbohydrates ESI-MS/MS Joyce Palsson, 2006 and Lipidomics and Lipidomics : Large-scale measurement of cellular metabolites and their levels -> combined with proteome: functional readout of a cellular state Limitations / challenges: - often low reproducibility of sample preparations - highly divers set of metabolites - large dynamic range : Large-scale measurement of cellular metabolites and their levels -> combined with proteome: functional readout of a cellular state Limitations / challenges: - often low reproducibility of sample preparations - highly divers set of metabolites - large dynamic range Lipidomics aim: To identify classify cellular inventory of lipids and lipid interacting factors -> pathobiological impact - low to medium throughput - reproducibility difficult Glycomics identifies cellular glycan components and glycan-interacting factors Glycan-array -> glycan-recognition proteins Impact: - antigen recognition - cell adhesion - cancer biology Glycan mass spec Glyco-gene chip Methods still under development http://www.functionalglycomics.org In silico predictions, organelle proteomics histocytomics, Joyce Palsson, 2006
tells about sub-cellular locations tells about sub-cellular locations Bioinformatics TargetP http://www.cbs.dtu.dk/services /TargetP/ PSORT http://www.psort.org/ Bioinformatics TargetP http://www.cbs.dtu.dk/services /TargetP/ PSORT http://www.psort.org/ Organellar proteomics Preparation / digestion -> 2DE MS Expasy -> Topology Prediction http://www.expasy.ch/tools /#proteome Eukaryotic protein sorting signals Expasy -> Topology Prediction http://www.expasy.ch/tools /#proteome Microscopy techniques -> tagging (GFP) -> antibody detection Histocytomics e.g. LSC (Laser Scanning Cytometry) or LES (Layered Expression Screening) Emanuelsson 2002 Spalding et al., 2010 Coulton, 2004 ChIP-chips Protein-DNA -> e.g. ChIP on chips Protein-Protein -> e.g. Antibody-based protein arrays Yeast-2Hybrid, Protein-chips Joyce Palsson, 2006 www.avivasysbio.com Amplification and analysis Wingren_Borrebaeck_2009 Protein-Protein -> Yeast two-hybrid screens All interactomics need to be validated!!! => often false positives!!! Isotopic tracing Joyce Palsson, 2006
looks at global and dynamic changes of metabolite levels over time looks at global and dynamic changes of metabolite levels over time metabolite identification A C E E A v 1 B v 2 C v 3 D v 4 v v 6 5 E v7 metabolic flux analysis pathway reconstruction -> integration of omics- from other sources http://www.nat.vu.nl/~ivo/flux/ Van Beek, Van Stokkum But: Method suffers from same shortcomings as metabolomics: -> sample prep reproducibility -> wide variety of metabolites -> large dynamic range http://www.nat.vu.nl/~ivo/flux/ If 14 C-label: scintillation counting Van Beek, Van Stokkum High-throughput approaches to determine cellular fitness or viability in response to genetic / environmental manipulation Some commonly used experimental approaches:! Phenotyping microarrays Phenotype arrays, RNAi-screens Joyce Palsson, 2006 http://www.biolog.com/pmtechdesover.html Integration of omics- => RNAi screens The Challenge: How to integrate extreme abundances of heterogeneous from very divers sources Genomics
Annotated Network reconstruction Network reconstruction signaling Annotated Network reconstruction Network reconstruction model signaling Annotated Model testing and validation Biochemical genetic methods model Revised assignments model functions signalling New predictions etc. The holy grail of systems biology: Automatically updated, -scale, comprehensive network reconstructions for any system of interest => Advanced projects for some model organisms (Human, mouse, yeast, E. coli) disorders Increasing medical impact Cancer biology Personalized drug design engineering Toxico Nutri Meta- Omics