systems biology: bioinformatics on complete biological system

Lars Juhl Jensen

Systems biologyBioinformatics on complete biological

systems

can a biologist fix a radio?

Lazebnik, Biochemistry, 2004

single gene studies

many experiments

knockout phenotype

Lazebnik, Biochemistry, 2004

everything about one gene

high-throughput biology

single technology

microarrays

one thing about every gene

systems biology

model complete systems

mathematical modeling

a simple system

Chen, Mol. Biol. Cell, 2004

simulation

Chen, Mol. Biol. Cell, 2004

many equations

Chen, Mol. Biol. Cell, 2004

many parameters

Chen, Mol. Biol. Cell, 2004

requires detailed knowledge

molecular networks

what is an interaction?

physical contact

stable interactions

transient interactions

interaction assays

yeast two-hybrid

fragment complementation

affinity purification

Jensen & Bork, Science, 2008

Jensen et al., Drug Discovery Today: TARGETS, 2004

spoke representation

Jensen et al., Drug Discovery Today: TARGETS, 2004

matrix representation

Jensen et al., Drug Discovery Today: TARGETS, 2004

interaction databases

BioGRIDGeneral Repository for Interaction Datasets

DIPDatabase of Interacting Proteins

IntAct

MINTMolecular Interactions Database

Exercise 1Go to http://thebiogrid.org

Query for human TYMS

Find the interaction partners

Check their sources

Think of possible problems

possibly many errors

purely high-throughput

one assay

one study

functional associations

guilt by association

STRING

experimental data

physical interactions

genetic interactions

Beyer et al., Nature Reviews Genetics, 2007

gene coexpression

curated knowledge

complexes

pathways

Letunic & Bork, Trends in Biochemical Sciences, 2008

genomic context

operons

Korbel et al., Nature Biotechnology, 2004

bidirectional promoters

Korbel et al., Nature Biotechnology, 2004

gene fusion

Korbel et al., Nature Biotechnology, 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology, 2004

visualization

Franceschini et al., Nucleic Acids Research, 2013

many databases

different formats

different identifiers

variable quality

not comparable

not same species

hard work

(students)

quality scores

von Mering et al., Nucleic Acids Research, 2005

calibrate vs. gold standard

von Mering et al., Nucleic Acids Research, 2005

homology-based transfer

Franceschini et al., Nucleic Acids Research, 2013

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

text mining

>10 km

too much to read

computer

as smart as a dog

teach it specific tricks

named entity recognition

comprehensive lexicon

cyclin dependent kinase 1

CDC2

flexible matching

cyclin dependent kinase 1

cyclin-dependent kinase 1

orthographic variation

CDC2

hCdc2

“black list”

SDS

co-mentioning

within documents

within paragraphs

within sentences

scoring scheme

NLPNatural Language Processing

grammatical analysis

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]

more precise

worse recall

related web resources

STITCH

STRING + 300k chemicals

drugs

metabolites

known drug targets

high-throughput screens

metabolic pathways

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?

general approach

suite of new resources

COMPARTMENTS

TISSUES

DISEASES

curated knowledge

experimental data

text mining

computational predictions

common identifiers

quality scores

visualization

compartments.jensenlab.org

tissues.jensenlab.org

thank you!