Rui Alves Ciencies Mèdiques Bàsiques Universitat de Lleida ralves@cmb.udl.es

1. From “omics” data to modeling-basednetworkreconstruction:Integrative Molecular Systems Biology with a view to biological design principles RuiAlves CienciesMèdiquesBàsiques Universitat de Lleida ralves@cmb.udl.es http://web.udl.es/usuaris/pg193845/Courses/Other%20Seminars/

2. Index of talk • Integrative in silicoreconstruction of Fe-S biogenesis pathway in yeast. • Design principles of bacterial signal transduction Two Component Systems. • Quantitative design of Gene Expression Profiles in yeast stress response.

3. Introduction • Understanding pathway assembly and function is fundamental to the understanding of how a cell works. • In annotated genomes, network of cellular pathways is “known”. • Mapping orthologues onto known maps (KEGG, BIOCYC, etc.). • However, regulatory topology is organism specific. • Nevertheless, reconstructing the topology of new pathways can not be done by mapping. • No maps available. • How to reconstruct?

4. Oriented reconstruction • Traditionally, identification & reconstruction of a pathway/circuit would entail painstaking, mostly blind, experimental work. • Currently, availability of “omics” data provides information to facilitate this task. • Computational Biology and Bioinformatics. • Integrate information, predict systemic behavior and rank hypothesis for experimental testing • Facilitates a better understanding of how cellular systems work.

5. Objective of the research line • Develop and apply coherent yet flexible framework where different computational methods and data sets are integrated to predict the connectivity of biological pathways & circuits. • Today: focus on the biology and the reconstruction of FeS cluster biogenesis in yeast S. cerevisiae.

6. S Protein Cysteine Protein Cysteine Fe Fe S Fe-S clusters • Iron-Sulfur Clusters are coordinated ions that participate in electron transfer. e- e-

7. What is known about FeSC biogenesis • About 15 different mitochondrial proteins are known to be involved in yeast. • The assembly process is ill-understood. • It is unclear how most of the proteins assemble as a pathway and how the activity of this pathway is regulated. • All 15 proteins have one thing in common.

8. Phenotype of FeSC machinery deletion mutants FeSC Dependent Protein Activity Fe Level WT D WT D FeSC dependent protein activity is impaired Fe Accumulates

9. Scaffold Scaffold Scaffold Scaffold Fe S FeSC FeSC Holo-P Apo-P Damaged FeSC Holo-P FeSC biogenesis in a nutshell Grx5 Isa1 Isa2 Isu1 Isu2 Nfu1 Atm1 Nfs1 Arh1 Yah1 Yfh1 Isd11 Ssq1 Jac1 Mge1 Synthesis (S) Transfer (T) Repair (R)

10. The reconstruction method Getproteinstructures (PDB, models) Identifyadditional Genes involved in process Identify Genes involved in process 2. Phylogeneticanalysis 1. Bibliometricanalysis 2. Interrogate 2H databases 3. In silicoproteindocking Process of interest Genes with similar co-evolutionprofiles List of reportedTwo-hybridinteractions List of predictedinteractions Humancuration ExpertKnowledge New Simulationexperiments Validatedmodels Createmathematicalmodelsforeachalternativenetwork Derive alternativenetworkstructures No ValidModel 4. Simulation and comparisonto experimental results Falsifiedmodels

11. 1. Literature-basednetworkreconstruction • Literature co-occurence of genes can be taken as a signal that they are functionaly related and maybe interact physically. • iHOP performes this type of analysis automatically.

12. Database of proteins in fullysequencedgenomes Database of profilesforeachprotein in eachorganism 2. Usingphylogeneticprofilestopredictproteininteractions Proteinsthat are present and absent in thesame set of genomes are likelytobeinvolved in thesameprocess and thereforeinteract. Sequence (Grx5) Protein id Grx5 Calculatecoincidenceindex.

13. 3. Low level study of docking interactions in silico Sequence with known structure. …SSQIE… …SSQEE… Homologue sequence for structure prediction. THREAD DOCK OPTIMIZE Differential scores for docking to different targets.

14. 4.Studying the effect of a protein: The modeling Grx5,… Nfs1-SSG Nfs1 • Use approximate formalism: • Power Law Formalism • No need for detailed mechanism. • Semi quantitative estimation of many parameter values. g<0 inhibits flux. g=0 no influence on flux. g>0 activates flux.

15. Studying the effect of a protein: the modelling • Create models for alternative networks. • Normalize equations and scan parameters to see what happens when a gene is deleted from the model. • Compare simulations with known systemic behavior to validate or invalidate alternatives.

16. Grx5 is involved in FeSC biogenesis in S. cerevisiae • Glutaredoxin: • Mediatesglutathionylationstate of Cysresidues. • Maymediateprotein-proteindisulfide bridge reduction (Belli et al. 2002, Tamarit et al. 2003, JBC). • FeSCcoordinate (mostly) withCysresidues. • Is Grx5 regulation of Cysreductionstate in anyspecificprotein(s) involved in FeSCbiogenesissufficientforphenotype?

17. Possible partners of Grx5 in FeSC biogenesis Bibliography Docking Phylogeny+ Docking Scaffolds Isu1 Isa1 Isa2 Nfs1 Grx5

18. 1 1 0.5 0.1 0.1 WT D Model reproduces effect of gene deletion on protein activity if Grx5 recovers Nfs1 activity FeSC Dependent Protein Activity 10000s of simulations 0.5 Recovering Nfs1 and Scaffold Not recovering Nfs1 and Scaffold Belli et al. 2002 MBC 13:1109

19. Model reproduces effect of gene deletion on protein activity if Grx5 recovers Nfs1 activity Fe Levels 10000s of simulations 1 1 WT D Not recovering Nfs1and Scaffold Recovering Nfs1 and Scaffold Belli et al. 2002 MBC 13:1109

20. No Predictions: Possible Modes of action for Grx5 Grx5 modulates Nfs1 and Scaffold activity/Interactions. Reproducing experimental phenotype? 6 9 Yes 3 Nfs1-Scaffold

21. Grx5 Scaffold Positive Control Negative Controls Grx5 interacts with scaffold in two-hybrid assay

22. The proteins and their function Alves & Sorribas 2007 BMC Systems Biology 1:10 Alves et. al. 2004 Proteins 56:354 Alves et. al. 2004 Proteins 57:481 Vilella et. al. 2004 Comp. Func. Genomics 5:328 Alves et. al. 2008 Current Bioinformatics, accepted

23. Metabolic Reconstruction: FeSC biogenesisThe view from here • Create a FLEXIBLE tool for other researchers. • Automation of text search 75% done; Phylogenetic profiling 75% done, Protein interactions 75% done, Automation of structural modeling & docking 0%. • Data sets very noise, human curation required & very important in the forseeable future.

24. The reconstruction method Getproteinstructures (PDB, models) Identifyadditional Genes involved in process Identify Genes involved in process 2. Phylogeneticanalysis 1. Bibliometricanalysis 2. Interrogate 2H databases 3. In silicoproteindocking Process of interest Add Genomics, Proteomics, Metabolomics, Fluxomics Genes with similar co-evolutionprofiles List of reportedTwo-hybridinteractions List of predictedinteractions Humancuration ExpertKnowledge New Simulationexperiments Validatedmodels Createmathematicalmodelsforeachalternativenetwork Derive alternativenetworkstructures No ValidModel Simulation and comparisonto experimental results Falsifiedmodels

25. Application to other systems • Fe-S Human, chimp, coli, subtilis, xanthus, albicans. • Signal transduction reconstruction in xanthus.

26. Pathway design is often organism specific • Fe-S biogenesis pathways shows variations in the different organisms we are analyzing (coli, human, chimp, xanthus, subtilis). • Set of proteins not always the same, surely regulation will also be different. • Why differences? • Random thing, that is it. • There are functional advantages to the alternative designs, this causes selection of different alternatives under different conditions and accounts for maintenance of the designs.

27. Index of talk • Integrative in silicoreconstruction of Fe-S biogenesis pathway in yeast. • Design principles of bacterial signal transduction Two Component Systems • Quantitative design of Gene Expression Profiles in yeast stress response

28. Differences in design are relevant • Previous work in gene circuits, signal transduction & metabolic pathways suggests that often the differences are relevant to the functionality of the system. • Understanding the selection and maintenance of these differences can helps us in discovering design principles for the system of interest.

29. S S* S S* R* R R* R Q2 Q2 Q1 Q1 Alternative sensor design in Two Component Systems Monofunctional Sensor Bifunctional Sensor Isbifunctionalityrelevantforthefunction of the TCS?

30. How to test this? 1 – Identify functional criteria that have physiological relevance. 2 – Create Mathematical models for the alternatives S-system has analytical steady state solution Analytical solutions → General features of the model that are independent of parameter values. 3 – Compare the behavior of the two models with respect to the functional criteria defined in 1. Comparison must be made appropriately, using Mathematically Controlled Comparisons. [Alves & Savageau Bioinformatics 16:534; 786]

31. X3 X1 X2 X4 X6 X5 A model with a monofunctional sensor Monofunctional Sensor

32. X3 X1 X2 X4 X6 X5 A model with a bifunctional sensor Bifunctional Sensor

33. AM AB AM AB Q Q Q Studying physiological differences of alternative designs 1

34. Bi/Mono Signal Amplification ratios are different for primary (Q1) or secondary (Q2) signals 2 1 0 1 0.5 0 Ratio of signalamplification 0 2.5 5 0 1.5 3 PrimarySignalSecondarySignal

35. Physiological Predictions • Bifunctional design lowers X6 signal amplification. • prefered when cross-talk is undesirable. (EnvZ) • Monofunctional design elevates X6 signal amplification. • prefered when cross-talk is desirable. (CheA) Bifunctionalityappearstoberelevantforthefunction of the TCS. Alves & Savageau 2003 Mol. Microbiology 48: 25

36. Graded vs. Switch-like behavior • Bacterial signal transduction systems can have graded responses. • They can also have switch-like responses [Igoshin et al. 2007 Mol Microbiol. 61:165]. Response Signal Are therespecifictopologicalelements in a TCS Module thatallowswitch-likebehavior?

37. X3 X1 X2 X4 X6 X5 Alternative topology for TCS modules Independent Phosphatase 7 alternative topologies X7 [Dead end complex]

38. Switch-likebehaviorispossible RR-P Signal

39. X3 X1 X2 X4 X6 X5 TCS modules that allow bistability Independent Phosphatase Topologies allowing for switching behavior X7 [Dead end complex]

40. Switch-like behavior is robust Parameter Values Signal Intensity Signal Intensity Signal Intensity Igoshin, Alves & Savageau 2008, Mol Microbiol, accepted

41. Summary of Design Principles • In TCS wefoundthat: • Bifunctionality vs. Monofunctionalitymaybeselectedbasedontherequirementsforcrosstalk. • Wiring of thecircuit (deadendcomplex and flux channelforthedephosphorylation of the RR, independent of the sensor) constraintdynamicbehavior (switch vs. graded). • Thisdoesnotensurethatswitchlikebehaviorwillbefoundbut: • Pointstowhereto look forit. • Helps in design of artificial TCS withswitch-likebehavior.

42. Design principles in signal transduction: The view from here • Analyze higher complexity TCS. • Analyze eukaryotic signal transduction. • Compare both.

43. Index of talk • Integrative in silicoreconstruction of Fe-S biogenesis pathway in yeast. • Design principles of bacterial signal transduction Two Component Systems • Quantitative design of Gene Expression Profiles (GEP) in yeast stress response

44. Quantitative design principles • The wiring of the network (topological design principles) constrains the possible range of dynamic responses for the network. • This response in principle has evolved to ensure survival under specific conditions (fine tuning). • Given the functional requirements for a specific cellular response it should be possible to explain the quantitative aspects of the response • Analysis of gene expression changes in heat shock response to test this hypothesis

45. How to test this? 1 – Identify functional criteria that have physiological relevance. 2 – Create mathematical model describing main aspects of the metabolic adaptation during the response. 3 – Decide range of allowable variation for gene expression & do large scale scanning of gene expression. 4 – Map gene expression onto model. 5 – Calculate how different GEP perform according to the functionality criteria.

46. C1- ATP synthesis. C2- Threalose synthesis. C3- NADPH synthesis. C4- Low accumulation of intermediates. C5- Burden of change. C6- Glycerol production. C7- Specific relationship in changes of activity between certain enzymes that are important to create an appropriate metabolic response. C8- Maintenance of F16P levels to keep a high glycolytic flux. Performance criteria 46

47. Glycogen Trehalose Modeling metabolic changes during heat shock HXT: Hexose transporters GLK: Glucokinase PFK: Phosphofructokinase TDH: Glyceraldhyde 3P dehydrogenase PYK: Pyruvate kinase TPS: Trehalose phosphate syntase G6PDH: Glucose-6-P dehydrogenase NADPH Curto et al. 1995 Math. Biosci. 130: 25 Voit, Radivoyevitch 2000 Bioinformatics 16: 1023

48. Glycogen Trehalose Allowableranges of gene expression NADPH SIMULATIONS To explain why expression of particular genes is changed, we scanned the gene expression space and translated that procedure into different gene expression profiles (GEP). Consider a set of possible values for each enzyme. Explore all possible combinations. Total: 4.637.360 hypothetical GEPs. GLK, TPS → [ 1, 2.5, 4, ..., 14.5, 16, 17.5, 19] HXT → [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] G6PDH → [1, 2, 3, 4, 5, 6, 7, 8] PFK, TDH, PYK → [ 0.25, 0.33, 0.5, 1, 2, 3, 4]

49. Selecting the GEP

50. % of total GEPs Fold change in gene expression GEP with adequate responses ■% of the change-folds before any selection ■% of the change-folds after selecting by ALL criteria HXT: Hexose transporters GLK: Glucokinase PFK: Phosphofructokinase TDH: Glyceraldhyde 3P dehydrogenase PYK: Piruvate kinase TPS: Trehalose phosphate syntase G6PDH: Glucose-6-P dehydrogenase Fullfilallcriteria: • SIMULATION: 0.06% GEPs(4238) • 3 experimental databases Vilaprinyo et al. 2006 BMC Bioinformatics. 7: 184 50