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Systems biology - Understanding biology at the systems level
 

Systems biology - Understanding biology at the systems level

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    Systems biology - Understanding biology at the systems level Systems biology - Understanding biology at the systems level Presentation Transcript

    • Systems biologyUnderstanding biology at the systems level Lars Juhl Jensen
    • traditional biology
    • can a biologist fix a radio?
    • Lazebnik, Biochemistry, 2004
    • one gene
    • one postdoc
    • all aspects
    • knockout phenotype
    • Lazebnik, Biochemistry, 2004
    • one gene
    • high-throughput biology
    • one technology
    • one lab
    • all genes
    • one aspect
    • systems biology
    • complete systems
    • all aspects
    • all genes
    • systems-level properties
    • two subfields
    • mathematical modeling
    • small systems
    • data integration
    • large systems
    • the system
    • mitotic cell cycle
    • grow and divide
    • one cell
    • two cells
    • four phases
    • G1 phase
    • growth
    • S phase
    • DNA replication
    • G2 phase
    • growth
    • M phase
    • cell division
    • regulation
    • gene expression
    • phosphorylation
    • targeted degradation
    • protein interactions
    • cell cycle modeling
    • core cell cycle
    • Chen, Mol. Biol. Cell, 2004
    • many equations
    • Chen, Mol. Biol. Cell, 2004
    • simulation
    • Chen, Mol. Biol. Cell, 2004
    • many parameters
    • Chen, Mol. Biol. Cell, 2004
    • requires detailed knowledge
    • cell cycle analysis
    • gene expression
    • cell cultures
    • synchronization
    • microarrays
    • time courses
    • Gauthier et al., Nucleic Acids Research, 2007
    • cycling genes
    • time of peak expression
    • exercise 1
    • http://cyclebase.org
    • S. cerevisiae
    • RNR1, RNR2, RNR3, RNR4
    • S. pombe
    • cdc22, suc22
    • which genes cycle?
    • do time courses agree?
    • do orthologs agree?
    • do paralogs agree?
    • protein networks
    • guilt by association
    • STRING
    • >1100 genomes
    • genomic context
    • gene fusion
    • Korbel et al., Nature Biotechnology, 2004
    • conserved neighborhood
    • Korbel et al., Nature Biotechnology, 2004
    • phylogenetic profiles
    • Korbel et al., Nature Biotechnology, 2004
    • protein interactions
    • Jensen & Bork, Science, 2008
    • genetic interactions
    • Beyer et al., Nature Reviews Genetics, 2007
    • gene coexpression
    • curated knowledge
    • Letunic & Bork, Trends in Biochemical Sciences, 2008
    • >10 km
    • text mining
    • Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
    • co-mentioning
    • NLPNatural Language Processing
    • different sources
    • Ensembl
    • RefSeq
    • BINDBiomolecular Interaction Network Database
    • BioGRIDGeneral Repository for Interaction Datasets
    • DIPDatabase of Interacting Proteins
    • IntAct
    • MINTMolecular Interactions Database
    • HPRDHuman Protein Reference Database
    • PDBProtein Data Bank
    • GEOGene Expression Omnibus
    • MIPSMunich Information center for Protein Sequences
    • Gene Ontology
    • BioCyc
    • KEGGKyoto Encyclopedia of Genes and Genomes
    • PIDNCI-Nature Pathway Interaction Database
    • Reactome
    • different formats
    • different names
    • CDC2
    • CDK1
    • P06493
    • not comparable
    • variable quality
    • confidence scores
    • calibrate to gold standard
    • von Mering et al., Nucleic Acids Research, 2005
    • transfer by orthology
    • von Mering et al., Nucleic Acids Research, 2005
    • combine scores
    • exercise 2
    • http://string-db.org
    • Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
    • changing parameters
    • high confidence only
    • experiments only
    • evidence viewers
    • which interact functionally?
    • which interact physically?
    • complex regulation
    • time of peak expression
    • protein interactions
    • temporal network
    • de Lichtenberg, Jensen et al., Science, 2005
    • dynamic vs. static subunits
    • de Lichtenberg, Jensen et al., Science, 2005
    • just-in-time assembly
    • de Lichtenberg, Jensen et al., Cell Cycle, 2007
    • evolutionary flexibility
    • orthologs and paralogs
    • Jensen, Jensen, de Lichtenberg et al., Nature, 2006
    • protein complexes
    • Jensen, Jensen, de Lichtenberg et al., Nature, 2006
    • exercise 3
    • http://cyclebase-string.jensenlab.org
    • network expansion
    • what does SML1 do?
    • when is SML1 expressed?
    • how does that make sense?
    • multi-layer regulation
    • phosphorylation
    • CDK substrates
    • low-throughput data
    • high-throughput data
    • NetPhosK
    • correlation
    • Jensen, Jensen, de Lichtenberg et al., Nature, 2006
    • Jensen, Jensen, de Lichtenberg et al., Nature, 2006
    • bias
    • correlated changes
    • removes the bias
    • Jensen, Jensen, de Lichtenberg et al., Nature, 2006
    • co-evolution
    • disease networks
    • human proteins
    • >8,000 disease terms
    • text mining
    • co-mentioning
    • exercise 4
    • http://diseases.jensenlab.org
    • TYMS disease associations
    • inspect the evidence
    • colorectal cancer network
    • chemical networks
    • STITCH
    • STRING + chemicals
    • PubChem compounds
    • >74,000 small molecules
    • experimental data
    • BindingDB
    • ChEMBL
    • PDSP KiPsycoactive Drug Screening Program
    • PDBProtein Data Bank
    • drug targets
    • CTDComparative Toxicogenomics Database
    • DrugBank
    • GLIDAGPCR-Ligand Database
    • Matador
    • TTDTherapeutic Target Database
    • metabolic pathways
    • BioCyc
    • KEGGKyoto Encyclopedia of Genes and Genomes
    • Reactome
    • text mining
    • co-mentioning
    • NLPNatural Language Processing
    • same issues as for proteins
    • only worse
    • exercise 5
    • http://stitch-db.org
    • chemical network for TYMS
    • Kuhn et al., Nucleic Acids Research, 2012
    • network expansion
    • which role has thymidylate?
    • which role has dUMP?
    • which role has Pemetrexed?