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Medical Research Methodology in Translational Medicine, University of Copenhagen, Copenhagen, August 24, 2010.
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Computational BiologySignaling networks and drug repositioning
Lars Juhl Jensen
sequence analysis
Jensen, Gupta et al., Journal of Molecular Biology, 2002
data integration
de Lichtenberg, Jensen et al., Science, 2005
text mining
Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
human variation
signaling networks
phosphoproteomics
in vivo phosphosites
kinases are unknown
sequence motifs
Miller, Jensen et al., Science Signaling, 2008
NetPhorest
data organization
Miller, Jensen et al., Science Signaling, 2008
automated pipeline
Miller, Jensen et al., Science Signaling, 2008
compilation of datasets
training and evaluation
motif atlas
179 kinases
89 SH2 domains
8 PTB domains
BRCT domains
WW domains
14-3-3 proteins
sequence specificity
kinase-specific
in vitro
network context
Linding, Jensen, Ostheimer et al., Cell, 2007
STRING
Jensen, Kuhn et al., Nucleic Acids Research, 2009
630 genomes
2.5 million proteins
genomic context
gene fusion
Korbel et al., Nature Biotechnology, 2004
phylogenetic profiles
Korbel et al., Nature Biotechnology, 2004
primary experimental data
physical interactions
Jensen & Bork, Science, 2008
gene coexpression
curated knowledge
Letunic & Bork, Trends in Biochemical Sciences, 2008
literature mining
not comparable
confidence scores
von Mering et al., Nucleic Acids Research, 2005
cross-species integration
Linding, Jensen, Ostheimer et al., Cell, 2007
putting it all together
NetworKIN
Linding, Jensen, Ostheimer et al., Cell, 2007
>2x better accuracy
use case
DNA damage response
Linding, Jensen, Ostheimer et al., Cell, 2007
experimental validation
ATM phosphorylates Rad50
Linding, Jensen, Ostheimer et al., Cell, 2007
drug repositioning
new uses for old drugs
drug–drug network
shared target(s)
chemical similarity
Tanimoto coefficients
Campillos & Kuhn et al., Science, 2008
Campillos & Kuhn et al., Science, 2008
similar drugs share targets
only trivial predictions
phenotypic similarity
chemical perturbations
phenotypic readouts
drug treatment
side effects
no database
package inserts
Campillos & Kuhn et al., Science, 2008
text mining
side-effect ontology
backtracking
Campillos & Kuhn et al., Science, 2008
side-effect correlations
Campillos & Kuhn et al., Science, 2008
GSC weighting
side-effect frequencies
Campillos & Kuhn et al., Science, 2008
raw similarity score
Campillos & Kuhn et al., Science, 2008
p-values
Campillos & Kuhn et al., Science, 2008
side-effect similarity
chemical similarity
Campillos & Kuhn et al., Science, 2008
confidence scores
drug–drug network
Campillos & Kuhn et al., Science, 2008
categorization
Campillos & Kuhn et al., Science, 2008
experimental validation
20 drug–drug relations
in vitro binding assays
Ki<10 µM for 11 of 20
cell assays
9 of 9 showed activity
summary
computational biology
network analysis
testable predictions
save much time in the lab
AcknowledgmentsNetPhorest.info
– Rune Linding– Martin Lee Miller– Francesca Diella– Claus Jørgensen– Michele Tinti– Lei Li– Marilyn Hsiung– Sirlester A. Parker– Jennifer Bordeaux– Thomas Sicheritz-Pontén– Marina Olhovsky– Adrian Pasculescu– Jes Alexander– Stefan Knapp– Nikolaj Blom– Peer Bork– Shawn Li– Gianni Cesareni– Tony Pawson– Benjamin E. Turk– Michael B. Yaffe– Søren Brunak
STRING-DB.org– Christian von Mering– Damian Szklarczyk– Michael Kuhn– Manuel Stark– Samuel Chaffron– Chris Creevey– Jean Muller– Tobias Doerks– Philippe Julien– Alexander Roth– Milan Simonovic– Jan Korbel– Berend Snel– Martijn Huynen– Peer Bork
Side effect– Monica Campillos– Michael Kuhn– Christian von Mering– Anne-Claude Gavin– Peer Bork
NetworKIN.info– Rune Linding– Gerard Ostheimer– Heiko Horn– Martin Lee Miller– Francesca Diella– Karen Colwill– Jing Jin– Pavel Metalnikov– Vivian Nguyen– Adrian Pasculescu– Jin Gyoon Park– Leona D. Samson– Rob Russell– Peer Bork– Michael Yaffe– Tony Pawson
larsjuhljensen
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