Systems biology: Bioinformatics on complete biological system
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Transcript of Systems biology: Bioinformatics on complete biological system
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Lars Juhl Jensen
Systems biologyBioinformatics on complete biological
systems
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can a biologist fix a radio?
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Lazebnik, Biochemistry, 2004
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single gene studies
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many experiments
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knockout phenotype
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Lazebnik, Biochemistry, 2004
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everything about one gene
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high-throughput biology
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single technology
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microarrays
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one thing about every gene
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systems biology
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model complete systems
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mathematical modeling
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a simple system
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Chen, Mol. Biol. Cell, 2004
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simulation
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Chen, Mol. Biol. Cell, 2004
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many equations
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Chen, Mol. Biol. Cell, 2004
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many parameters
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Chen, Mol. Biol. Cell, 2004
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requires detailed knowledge
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molecular networks
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what is an interaction?
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physical contact
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stable interactions
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transient interactions
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interaction assays
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yeast two-hybrid
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fragment complementation
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affinity purification
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Jensen & Bork, Science, 2008
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Jensen et al., Drug Discovery Today: TARGETS, 2004
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spoke representation
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Jensen et al., Drug Discovery Today: TARGETS, 2004
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matrix representation
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Jensen et al., Drug Discovery Today: TARGETS, 2004
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interaction databases
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BioGRIDGeneral Repository for Interaction Datasets
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DIPDatabase of Interacting Proteins
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IntAct
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MINTMolecular Interactions Database
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Exercise 1Go to http://thebiogrid.org
Query for human TYMS
Find the interaction partners
Check their sources
Think of possible problems
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possibly many errors
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purely high-throughput
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one assay
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one study
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functional associations
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guilt by association
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STRING
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experimental data
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physical interactions
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genetic interactions
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Beyer et al., Nature Reviews Genetics, 2007
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gene coexpression
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curated knowledge
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complexes
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pathways
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Letunic & Bork, Trends in Biochemical Sciences, 2008
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genomic context
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operons
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Korbel et al., Nature Biotechnology, 2004
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bidirectional promoters
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Korbel et al., Nature Biotechnology, 2004
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gene fusion
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Korbel et al., Nature Biotechnology, 2004
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phylogenetic profiles
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Korbel et al., Nature Biotechnology, 2004
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visualization
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Franceschini et al., Nucleic Acids Research, 2013
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many databases
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different formats
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different identifiers
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variable quality
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not comparable
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not same species
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hard work
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(students)
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quality scores
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von Mering et al., Nucleic Acids Research, 2005
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calibrate vs. gold standard
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von Mering et al., Nucleic Acids Research, 2005
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homology-based transfer
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Franceschini et al., Nucleic Acids Research, 2013
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Exercise 2Query STRING for human TYMS
Show network in confidence mode
Show up to 20 interaction partners
Show only experimental evidence
Show also low-confidence links
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text mining
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>10 km
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too much to read
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computer
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as smart as a dog
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teach it specific tricks
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named entity recognition
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comprehensive lexicon
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cyclin dependent kinase 1
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CDC2
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flexible matching
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cyclin dependent kinase 1
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cyclin-dependent kinase 1
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orthographic variation
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CDC2
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hCdc2
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“black list”
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SDS
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co-mentioning
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within documents
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within paragraphs
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within sentences
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scoring scheme
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NLPNatural Language Processing
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grammatical analysis
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Gene and protein namesCue words for entity recognitionVerbs for relation extraction
[nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]]is controlled by[nxpg HAP1]
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more precise
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worse recall
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related web resources
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STITCH
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STRING + 300k chemicals
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drugs
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metabolites
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known drug targets
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high-throughput screens
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metabolic pathways
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Exercise 3Go to http://stitch-db.org
Query for human TYMS
What is the role of thymidylate?
What is the role of dUMP?
What is the role of Permetrexed?
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general approach
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suite of new resources
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COMPARTMENTS
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TISSUES
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DISEASES
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curated knowledge
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experimental data
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text mining
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computational predictions
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common identifiers
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quality scores
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visualization
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compartments.jensenlab.org
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tissues.jensenlab.org
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thank you!
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