Multiscale Modelling

1. MultiscaleModelling Project Fallot Tariq Abdulla December 2009

2. Outline • Information Modelling – Ontologies, XML and databases • Petri nets – graph based representation of networks and pathways • Network Analysis – network type, motifs • Integration of models

3. Ontologies Provide a common, structured vocabulary, in order to overcome confusion in terminology. Facilitate the integration and querying of heterogeneous datasets (and, increasingly, models).

4. Gene Ontology Collaboration between model organism databases – thus inherently cross-species Reference ontology – for more specific annotation, we may develop application ontologies, that reference GO and other reference ontologies Split into 3 seperate ontologies: Biological Process, Cellular Component and Molecular Function

5. Gene Ontology: AmiGO

6. Gene Ontology: AmiGO

7. Rat Genome Database Nkx2.5

8. Rat Genome Database Jagged1

9. (Horridge et al. 2009) Properties Functional Inverse Functional Symmetric Transitive Reflexive Irreflexive

10. Properties

11. Automatic Classification

12. Automatic Classification

13. place arc inhibitory arc transition token Petri Nets t2 p4 p1 p3 t1 p2 t3 3

14. vmax = Kcat[E] (Gilbert, et al. 2006)

15. SBML – Enzyme Reaction

16. KEGG Representation Is this straightforward?

17. (Heiner, Koch and Will 2004)

18. (Heiner, Koch and Will 2004)

20. Pathways: structural differences Metabolic Networks Signal Transduction Networks (Breitling, et al. 2008)

21. Phosphorylation Kinase Signalling Protein Phosphorylated Form Phosphotase

22. Notch Signalling (Artavanis-Tsakonas, Rand and Lake 1999)

23. Hybrid Petri Nets • Places and Transitions can be either discrete or continuous

24. HFPN: Notch Signalling

25. HFPN: Notch Signalling

26. P1 T1 P2 1 m1/2 m1 m2 m1/2.5 XML Representation of HFPN <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE hybridFunctionalPetriNet SYSTEM "SampleHFPNet.dtd"> <HFPN> <place id="P1" type="continuos" variableName="m1"/> <place id="P2" type="continuos" variableName="m2"/> <transition id="T1" speedFunction="m1/2.5" type="continuos"/> <arc from="P1" to="T1" type="normal" weight="1"/> <arc from="T1" to="P2" type="normal" weight="m1/2"/> </HFPN>

27. How Can we understand this? Network Analysis!

28. Signalling Pathways are robust because: • They are small world, scale free networks Power Law Distribution: P(k)∼k−γ

29. Signalling Pathways are robust because: • There are redundant pathways, feedback loops, and combinatorial complex • Cross-talk between pathways provide additional sites to regulate signalling

30. Network Motifs

31. Network Motifs (Prill, Iglesias and Levchenko 2005)

32. Model Checking • Liveness • Reachability • P and T invariants

33. Mining Pathway Information • Pathway databases are either created by curators, or through text mining of the literature • Curated databases tend to be higher quality, but the breadth may be narrower

34. Levels of Abstraction

35. Why model? • Generate new insights • Make testable predictions • Test conditions that may be difficult/impossible to study in vitro / in vivo • Rule out particular explanations for an experimental observation • Help identify what is right/wrong with an hypothesis

36. Analysis and Interpretation • Validation: do the model results match experimental data? • Prediction: • Sensitivity analysis • Knockout experiments

37. Information Management • Identify building blocks / submodels • Database • Models, model components • Behaviours • Properties • Component reuse • Version control • Model checking • Maintaining temporal-logical properties

38. A Proposition: • Find out the expression of Delta and Notch in the precursor cells of the Heart fields at an early stage • Simulate to find if the patterning corresponds to what is expected

39. Conclusion • By encoding models, literature and experimental results in XML, and storing them in web-accessible databases, intermediated by ontologies, we facilitate more holistic approaches. • A range of modelling are appropriate to different levels of scale • In the places where these can begin to be integrated, there is insight to be gained in silico

40. References • Horridge, Matthew, Simon Jupp, Georgina Moulton, Alan Rector, Robert Stevens, and Chris Wroe. • "A Practical Guide To Building OWL Ontologies Using Protégé 4." CO-ODE. October 16, 2007. • http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial.pdf • Gilbert, David, et al. "Computational methodologies for modelling, analysis and simulation of signalling networks." Breifings in Bioinformatics 7, no. 4 (2006): 339-353. • Heiner, Monika, Ina Koch, and Jürgen Will. "Model validation of biological pathways using Petri nets—demonstrated for apoptosis." Biosystems 75 (2004): 15-28. • Breitling, Rainer, David Gilbert, Monika Heiner, and Richard Orton. "A structured approach for the engineering of biochemical network models, illustrated for signaling pathways." Briefings in Bioinformatics 9, no. 5 (2008): 404-421. • Matsuno, Hiroshi, Ryutaro Murakami, Rie Yamane, Naoyuki Yamasaki, Sachie Fujita, and HarukaYoshimori. "Boundary Formation by Notch Signalling in Drosophila Multicellular Systems: Experimental Observations and Gene Network Modeling by Genomic Object Net." Pacific Symposium on Biocomputing. Kauai, Hawaii: World Scientific, 2003. 152-163. • Artavanis-Tsakonas, Spyros, Matthew D. Rand, and Robert J. Lake. "Notch Signaling: Cell Fate Control and Signal Integration in Development." Science 284 (1999): 770-776. • Prill, Rober J., Pablo A. Iglesias, and Andre Levchenko. "Dynamic Properties of Network Motifs Contribute to Biological Network Organization." PLOS Biology 3, no. 11 (2005): 1881-1892.

41. Further Reading Fisher, Steven A., Lowell B. Langille, and Deepak Srivastava. "Apoptosis During Cardiovascular Development." Circulation Research, 2000: 856-864. • Gittenberger-de Groot, A. C., and R. E. Poelmann. "A Subpopulation of Apoptosis-Prone Cardiac Neural Crest Cells Targets to the Venous Pole: Multiple Functions in Heart Development?" Developmental Biology, 1999: 271-286. • Barabási, Albert-László, and Zoltán N. Oltvai. "Network Biology: Understanding the Cell’s Functional Organization." Nature Reviews: Genetics, 2004: 101-113. • Rector, Alan, Jeremy Rogers, and Thomas Bittner. "Granularity scale and collectivity: when size does and does not matter." Journal of Biomedical Informatics, no. 39 (2006): 333-349. • Fisher, Jasmin, and Thomas A Henzinger. "Executable cell biology." NATURE BIOTECHNOLOGY 25, no. 11 (2007): 1239-1249. • Novere, Nicholas Le, Melanie Courtot, and Camille Laibe. "Adding Semantics in Kinetics Models of Biochemical Pathways." 2nd International ESCEC Symposium on Experimental Standard Conditions on Enzyme Characterizations.Rhein: BeilsteinInstitut, 2006. 137-153. • Niessen, Kyle, and AlyKarsan. "Notch Signalling in Cardiac Development." Circulation Research, 2008: 1169-1181. • Walker D C, Southgate J S, Hill G, Holcombe M, Hose D R, Wood S M, MacNeil S and Smallwood R H (2004) The Epitheliome: modelling the social behaviour of cells. BioSystems 76:89-100