Adding missing post-transcriptional regulations to a regulatory network

Transcription

1 Adding missing post-transcriptional regulations to a regulatory network Carito Guziolowski, Sylvain Blachon, Anne Siegel Project Symbiose - IRISA-INRIA - Rennes Journée satellite JOBIM 2008

2 Approach Objective: confront regulatory knowledge with experimental data Tool: Qualitative constraint modeling, causal rules. Results: diagnosis of shift-equilibria Curation of network models Prediction of fluctuation of protein/gene expression Advantage: Large-scale networks (> 1000 products) Disadvantage: No system dynamics

3 Consistency check process Concentration changes add data Experiments validate with Prediction yes System of constraints Consistent? no Influence network model correction Diagnosis Bioquali

4 Input: Influence network Signed and oriented graph Nodes: mrna / TF active proteins, protein complexes? Edges: A > C : Increase of product A influences production of C + : activation : inhibition Bindings, phosphorylation, sequestration, release...?

5 Input: Influence network Signed and oriented graph Nodes: mrna / TF active proteins, protein complexes? Edges: A > C : Increase of product A influences production of C + : activation : inhibition Bindings, phosphorylation, sequestration, release...?

6 Input: Influence network Signed and oriented graph Nodes: mrna / TF active proteins, protein complexes? Edges: A > C : Increase of product A influences production of C + : activation : inhibition Bindings, phosphorylation, sequestration, release...?

7 Input: Qualitative experimental data Compare 2 stable conditions of a cell Map observations into {+, } Examples: Microarray outputs: Statistically significant up/down regulated genes Correlated vs anti-correlated changes with a phenotype Literature: Experimentally observed differentially expressend genes

8 Consistency: causal boolean rule The variation of one molecule in the network must be explained by an influence received from at least one of its predecessors in the network (Siegel et al 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

9 Consistency: Validation and Lessons Validated on E. coli transcriptional network (Guziolowski et al JBPC) mrna variation active protein variation Problems Inconsistencies False positive predictions Unfixed values (too many) Solutions Missing post-transcriptional interactions Separate mrna from active proteins Refine general rule into specific boolean rules (avoid dynamic) All solutions include post-transcriptional reasoning

10 Consistency: Validation and Lessons Validated on E. coli transcriptional network (Guziolowski et al JBPC) mrna variation active protein variation Problems Inconsistencies False positive predictions Unfixed values (too many) Solutions Missing post-transcriptional interactions Separate mrna from active proteins Refine general rule into specific boolean rules (avoid dynamic) All solutions include post-transcriptional reasoning

11 Consistency: Validation and Lessons Validated on E. coli transcriptional network (Guziolowski et al JBPC) mrna variation active protein variation Problems Inconsistencies False positive predictions Unfixed values (too many) Solutions Missing post-transcriptional interactions Separate mrna from active proteins Refine general rule into specific boolean rules (avoid dynamic) All solutions include post-transcriptional reasoning

12 Consistency: Validation and Lessons Validated on E. coli transcriptional network (Guziolowski et al JBPC) mrna variation active protein variation Problems Inconsistencies False positive predictions Unfixed values (too many) Solutions Missing post-transcriptional interactions Separate mrna from active proteins Refine general rule into specific boolean rules (avoid dynamic) All solutions include post-transcriptional reasoning

13 Consistency: Validation and Lessons Validated on E. coli transcriptional network (Guziolowski et al JBPC) mrna variation active protein variation Problems Inconsistencies False positive predictions Unfixed values (too many) Solutions Missing post-transcriptional interactions Separate mrna from active proteins Refine general rule into specific boolean rules (avoid dynamic) All solutions include post-transcriptional reasoning

14 Applications Escherichia coli Goal: validate approach Transcriptional network large-scale: 1763 nodes, 4491 edges Hierarchical topology RegulonDB (Salgado et al. 2006) EWING network Goal: analyse consistency Signalling network middle-scale 130 nodes, 325 edges Complex topology Stoll et al. ICSB 2007

15 Applications: E. coli (1) Input data: Experimental condition: exponential-stationary growth shift. 45 literature curated gene variations (RegulonDB) Inconsistency Inconsistent data with network Proposed correction (Atlung et al. 1996) External signal Consistent after adding post-transcriptional effect 0,01% of network products changed as {+, }

16 Applications: E. coli (1) Input data: Experimental condition: exponential-stationary growth shift. 45 literature curated gene variations (RegulonDB) Inconsistency Inconsistent data with network Proposed correction (Atlung et al. 1996) External signal Consistent after adding post-transcriptional effect 0,01% of network products changed as {+, }

17 Applications: E. coli (1) Input data: Experimental condition: exponential-stationary growth shift. 45 literature curated gene variations (RegulonDB) Inconsistency Inconsistent data with network Proposed correction (Atlung et al. 1996) External signal Consistent after adding post-transcriptional effect 0,01% of network products changed as {+, }

18 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 the concentration of the limiting subunit A, B observed, C protein complex A, B observed, C protein complex. B limited A B C +? A B C + C NOT predicted C predicted Applied to the IHF protein complex in E. coli 30% products of the network changed as {+, }

19 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 the concentration of the limiting subunit A, B observed, C protein complex A, B observed, C protein complex. B limited A B C +? A B C + C NOT predicted C predicted Applied to the IHF protein complex in E. coli 30% products of the network changed as {+, }

20 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 the concentration of the limiting subunit A, B observed, C protein complex A, B observed, C protein complex. B limited A B C +? A B C + C NOT predicted C predicted Applied to the IHF protein complex in E. coli 30% products of the network changed as {+, }

21 Applications: E. coli (3) Validation of predictions Compared to microarray outputs (Faith et al PLoS Biology) 80% of the reported changes were in agreement And the rest? Expression Data ihfa = + ihfb = fic = +

22 Applications: E. coli (3) Validation of predictions Compared to microarray outputs (Faith et al PLoS Biology) 80% of the reported changes were in agreement And the rest? Expression Data ihfa = + ihfb = fic = + Prediction crp = ompa = rpod = rpos = + IHF =

23 Applications: E. coli (3) Validation of predictions Compared to microarray outputs (Faith et al PLoS Biology) 80% of the reported changes were in agreement And the rest? Expression Data ihfa = + ihfb = fic = + Prediction crp = ompa = rpod = rpos = + IHF = Rsd protein forms a complex with RpoD preventing it to bind RNA-polymerase (Jishage and Ishihama 1998) active RpoD predicted not rpod mrna

24 Applications: E. coli (3) Validation of predictions Compared to microarray outputs (Faith et al PLoS Biology) 80% of the reported changes were in agreement And the rest? Expression Data ihfa = + ihfb = fic = + Prediction crp = ompa = rpod = rpos = + IHF = Rsd protein forms a complex with RpoD preventing it to bind RNA-polymerase (Jishage and Ishihama 1998) active RpoD predicted not rpod mrna

25 Applications: E. coli (3) Validation of predictions Compared to microarray outputs (Faith et al PLoS Biology) 80% of the reported changes were in agreement And the rest? Expression Data ihfa = + ihfb = fic = + Prediction crp = ompa = rpod = rpos = + IHF = Rsd protein forms a complex with RpoD preventing it to bind RNA-polymerase (Jishage and Ishihama 1998) active RpoD predicted not rpod mrna

26 Ewing s network Include protein complex rule Many post-transcriptional interactions Dividing mrna from active protein (Baumuratova et al. SBMC 2008) Before After Consequences: Distinguish the type of predicted molecule New way of modeling phosphorylations

27 Ewing: Modelling phosphorylation The RB1 example active protein RB1 = hypophosphorylated RB1 Old model: New one:

28 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 the same moment Old model: New one:

29 Ewing: Modelling competitions(2) Model: Active RB1 wins active (E2F.) in the cell cycle S control (DeGregori 2002 BBA) Known behaviour: (E2F.) RB1 E2F:RB1 cc S We need is to add a new qualitative boolean rule: f(e2f, RB1) > cell cycle S

30 Ewing: Modelling competitions(2) Model: Active RB1 wins active (E2F.) in the cell cycle S control (DeGregori 2002 BBA) Known behaviour: (E2F.) RB1 E2F:RB1 cc S We need is to add a new qualitative boolean rule: f(e2f, RB1) > cell cycle S

31 Conclusion Reasoning from a general rule allow us to retrieve missing post-transcriptional mechanisms (E. coli) Any given literature curated network model may be post-transcriptionally refined (Ewing): Division between mrna/active protein Treating phosphorylation interactions Inhibitor-activator competition models mapped into logical functions Direct results of including post-transcriptional: Better validation of experimental outputs Enlarging predictions to (mrna, active protein, protein complex) Boolean rules may be appropiate to describe equilibria-shifts (work in progress) New Bioquali: including logical rules

32 Acknowledgements: Rennes Anne Siegel Sylvain Blachon Michel Le Borgne Jeremy Gruel Ovidiu Radulescu Philippe Veber Tatiana Baumaratova SITCON project bioinfo-out.curie.fr/projects/sitcon/

33 Merci!