Multiscale Modelling Project Fallot Tariq Abdulla December 2009

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

Ontologies 1.Provide a common, structured vocabulary, in order to overcome confusion in terminology. 2.Facilitate the integration and querying of heterogeneous datasets (and, increasingly, models).

Gene Ontology 1.Collaboration between model organism databases – thus inherently cross-species 2.Reference ontology – for more specific annotation, we may develop application ontologies, that reference GO and other reference ontologies 3.Split into 3 seperate ontologies: Biological Process, Cellular Component and Molecular Function

Gene Ontology: AmiGO

Rat Genome Database Nkx2.5

Rat Genome Database Jagged1

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

Properties

Automatic Classification

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

(Gilbert, et al. 2006) v max = K cat [E]

SBML – Enzyme Reaction

KEGG Representation Is this straightforward?

(Heiner, Koch and Will 2004)

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

Phosphorylation Kinase Phosphorylated Form Phosphotase Signalling Protein

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

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

HFPN: Notch Signalling

XML Representation of HFPN m1m2 T1 1m1/2 P1P2 m1/2.5

How Can we understand this? Network Analysis!

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

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

Network Motifs

(Prill, Iglesias and Levchenko 2005)

Model Checking Liveness Reachability P and T invariants

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

Levels of Abstraction

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

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

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

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

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

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, Gilbert, David, et al. "Computational methodologies for modelling, analysis and simulation of signalling networks." Breifings in Bioinformatics 7, no. 4 (2006): Heiner, Monika, Ina Koch, and Jürgen Will. "Model validation of biological pathways using Petri nets— demonstrated for apoptosis." Biosystems 75 (2004): 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): Matsuno, Hiroshi, Ryutaro Murakami, Rie Yamane, Naoyuki Yamasaki, Sachie Fujita, and Haruka Yoshimori. "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, Artavanis-Tsakonas, Spyros, Matthew D. Rand, and Robert J. Lake. "Notch Signaling: Cell Fate Control and Signal Integration in Development." Science 284 (1999): 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):

Further Reading Fisher, Steven A., Lowell B. Langille, and Deepak Srivastava. "Apoptosis During Cardiovascular Development." Circulation Research, 2000: 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: Barabási, Albert-László, and Zoltán N. Oltvai. "Network Biology: Understanding the Cell’s Functional Organization." Nature Reviews: Genetics, 2004: 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): Fisher, Jasmin, and Thomas A Henzinger. "Executable cell biology." NATURE BIOTECHNOLOGY 25, no. 11 (2007): 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: Beilstein Institut, Niessen, Kyle, and Aly Karsan. "Notch Signalling in Cardiac Development." Circulation Research, 2008: 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