adding missing post-transcriptional regulations to a ...introduction qualitative data consistency...

Introduction Qualitative data Consistency Applications Conclusion

Adding missing post-transcriptionalregulations to a regulatory network

Carito Guziolowski, Sylvain Blachon, Anne SiegelProject Symbiose - IRISA-INRIA - Rennes

Journee satellite JOBIM 2008

Approach

Objective: confront regulatory knowledge withexperimental dataTool: Qualitative constraint modeling, causal rules.Results: diagnosis of shift-equilibria

Curation of network modelsPrediction of fluctuation of protein/gene expression

Advantage: Large-scale networks (> 1000 products)Disadvantage: No system dynamics

Consistency check process

System ofconstraints

Concentrationchanges

Influencenetwork

Consistent?

Diagnosis

PredictionExperiments

model correction

validate withadd data

Bioquali → www.irisa.fr/symbiose/bioquali/

Input: Influence network

Signed and oriented graphNodes:

mRNA / TFactive proteins, protein complexes?

Edges:A −− > C :Increase of product A influences production of C+ : activation– : inhibitionBindings, phosphorylation, sequestration, release... ?

Input: Qualitative experimental data

Compare 2 stable conditions of a cellMap observations into {+, –}Examples:

Microarray outputs:Statistically significant up/down regulated genesCorrelated vs anti-correlated changes with a phenotype

Literature:Experimentally observed differentially expressend genes

Consistency: causal boolean rule

”The variation of one molecule in the network must be explainedby an influence received from at least one of its predecessors inthe network” (Siegel et al. 2006 Biosystems)

General rule : transcriptional regulations

A, B observed A, B observed. A, B, C observed.

A B C+ + +– – –

A B C+ – ?– + ?

A B C+ + –– – +

C predicted C NOT predicted.unfixed INCONSISTENCY

Consistency: Validation and Lessons

Validated on E. coli transcriptional network (Guziolowski et al.2006 JBPC)

mRNA variation ≈ active protein variation

ProblemsInconsistencies

False positive predictions

Unfixed values (too many)

SolutionsMissing post-transcriptionalinteractions

Separate mRNA from activeproteins

Refine general rule into specificboolean rules (avoid dynamic)

All solutions include post-transcriptional reasoning

Applications

Escherichia coliGoal: validate approachTranscriptional networklarge-scale: 1763 nodes,4491 edgesHierarchical topologyRegulonDB (Salgado et al.

EWING networkGoal: analyse consistencySignalling networkmiddle-scale 130 nodes,325 edgesComplex topologyStoll et al. ICSB 2007

Applications: E. coli (1)

Input data:Experimental condition: exponential-stationary growth shift.45 literature curated gene variations (RegulonDB)

Inconsistency

Inconsistent data with networkProposed correction (Atlung et al.

1996) External signal

Consistent after adding post-transcriptional effect0,01% of network products changed as {+, –}

Inconsistency

Applications: E. coli (2)New rule added: protein-complex behaviour rule

”The weakest takes it all” (Radulescu et al. FOSBE 2007)

The concentration of a protein complex at the steady state follows theconcentration of the limiting subunit

A, B observed, Cprotein complex

A, B observed, Cprotein complex. B

limited

A B C+ – ?

A B C+ – –

C NOT predicted C predicted

Applied to the IHF protein complex in E. coli30% products of the network changed as {+, –}

limited

A B C+ – ?

A B C+ – –

limited

A B C+ – ?

A B C+ – –

Validation of predictionsCompared to microarray outputs (Faith et al. 2007 PLoS Biology)80% of the reported changes were in agreement

And the rest ?

Expression DataihfA = +ihfB = –fic = +

And the rest ?

Predictioncrp = –

ompA = –rpoD = –rpoS = +IHF = –

And the rest ?

Predictioncrp = –

Rsd protein forms a complex with RpoD preventing it tobind RNA-polymerase (Jishage and Ishihama 1998)active RpoD predicted not rpoD mRNA

And the rest ?

Predictioncrp = –

And the rest ?

Predictioncrp = –

Ewing’s network

Include protein complex ruleMany post-transcriptional interactions → Dividing mRNAfrom active protein (Baumuratova et al. SBMC 2008)

Before After

Consequences:Distinguish the type of predicted moleculeNew way of modeling phosphorylations

Ewing: Modelling phosphorylation

The RB1 exampleactive protein RB1 = hypophosphorylated RB1

Old model: New one:

Ewing: Modelling competitions (1)The RB1, (E2F.) example

RB1 does not directly inhibit (E2F.)Both proteins and the cell cycle S may be active (+) at thesame moment

Old model: New one:

Ewing: Modelling competitions(2)

Active RB1 wins active (E2F.) in the cell cycle S control (DeGregori 2002 BBA)

Model:

Known behaviour:(E2F.) RB1 E2F:RB1 cc S

+ + + –– + – –+ – – +– – – –

We need is to add a new qualitative boolean rule:f(E2F, RB1) −− > cell cycle S

Ewing: Modelling competitions(2)

Active RB1 wins active (E2F.) in the cell cycle S control (DeGregori 2002 BBA)

Model:

Known behaviour:(E2F.) RB1 E2F:RB1 cc S

+ + + –– + – –+ – – +– – – –

We need is to add a new qualitative boolean rule:f(E2F, RB1) −− > cell cycle S

Conclusion

Reasoning from a general rule allow us to retrieve missing post-transcriptionalmechanisms (E. coli)

Any given literature curated network model may be post-transcriptionallyrefined (Ewing):

Division between mRNA/active proteinTreating phosphorylation interactionsInhibitor-activator competition models mapped into logical functions

Direct results of including post-transcriptional:Better validation of experimental outputsEnlarging predictions to (mRNA, active protein, protein complex)

Boolean rules may be appropiate to describe equilibria-shifts (work in progress)

New Bioquali: including logical rules

Acknowledgements:

RennesAnne SiegelSylvain BlachonMichel Le BorgneJeremy GruelOvidiu RadulescuPhilippe VeberTatiana Baumaratova

SITCON projectbioinfo-out.curie.fr/projects/sitcon/

Merci!

adding missing post-transcriptional regulations to a ...introduction qualitative data consistency...

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