1. Spreading Semantics Over Biology Phillip Lord Newcastle University

2. Overview Conclusions Data Integration in ComparaGRID Annotation in CARMEN and CISBAN Computing with Semantics The future

3. Conclusions Thin Semantics is Good More Semantics is Better Shared Semantics is Wonderful

4. Key Problems Scalability –Both in technology and processes Usability Autonomy

5. What is the most widely abused word in life sciences? ID PRIO_HUMAN STANDARD; PRT; 253 AA. AC P04156; DT 01-NOV-1986 (Rel. 03, Created) DT 01-NOV-1986 (Rel. 03, Last sequence update) DT 20-AUG-2001 (Rel. 40, Last annotation update) DE Major prion protein precursor (PrP) (PrP27-30) (PrP33-35C) (ASCR). GN PRNP. OS Homo sapiens (Human). OC Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; OC Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo. OX NCBI_TaxID=9606; RN [1] RP SEQUENCE FROM N.A. RX MEDLINE=86300093; PubMed=3755672; RA Kretzschmar H.A., Stowring L.E., Westaway D., Stubblebine W.H., RA Prusiner S.B., Dearmond S.J.; RT "Molecular cloning of a human prion protein cDNA."; RL DNA 5:315-324(1986). RN [2] RP SEQUENCE OF 8-253 FROM N.A. RX MEDLINE=86261778; PubMed=3014653; RA Liao Y.-C.J., Lebo R.V., Clawson G.A., Smuckler E.A.; RT "Human prion protein cDNA: molecular cloning, chromosomal mapping, RT and biological implications."; RL Science 233:364-367(1986). RN [3] RP SEQUENCE OF 58-85 AND 111-150 (VARIANT AMYLOID GSS). RX MEDLINE=91160504; PubMed=1672107; RA Tagliavini F., Prelli F., Ghiso J., Bugiani O., Serban D., RA Prusiner S.B., Farlow M.R., Ghetti B., Frangione B.; RT "Amyloid protein of Gerstmann-Straussler-Scheinker disease (Indiana RT kindred) is an 11 kd fragment of prion protein with an N-terminal RT glycine at codon 58."; RL EMBO J. 10:513-519(1991). RN [4] RP STRUCTURE BY NMR OF 118-221. RX MEDLINE=20359708; PubMed=10900000; RA Calzolai L., Lysek D.A., Guntert P., von Schroetter C., Riek R., RA Zahn R., Wuethrich K.; RT "NMR structures of three single-residue variants of the human prion RT protein."; RL Proc. Natl. Acad. Sci. U.S.A. 97:8340-8345(2000). CC -!- FUNCTION: THE FUNCTION OF PRP IS NOT KNOWN. PRP IS ENCODED IN THE CC HOST GENOME AND IS EXPRESSED BOTH IN NORMAL AND INFECTED CELLS. CC -!- SUBUNIT: PRP HAS A TENDENCY TO AGGREGATE YIELDING POLYMERS CALLED CC "RODS". CC -!- SUBCELLULAR LOCATION: ATTACHED TO THE MEMBRANE BY A GPI-ANCHOR. CC -!- POLYMORPHISM: THE FIVE TANDEM OCTAPEPTIDE REPEATS REGION IS HIGHLY CC UNSTABLE. INSERTIONS OR DELETIONS OF OCTAPEPTIDE REPEAT UNITS ARE CC ASSOCIATED TO PRION DISEASE. FT SIGNAL 1 22 FT CHAIN 23 230 MAJOR PRION PROTEIN. FT PROPEP 231 253 REMOVED IN MATURE FORM (BY SIMILARITY). FT LIPID 230 230 GPI-ANCHOR (BY SIMILARITY). FT CARBOHYD 181 181 N-LINKED (GLCNAC...) (PROBABLE). FT DISULFID 179 214 BY SIMILARITY. FT DOMAIN 51 91 5 X 8 AA TANDEM REPEATS OF P- H-G-G-G-W-G- FT Q. FT REPEAT 51 59 1. FT REPEAT 60 67 2. FT REPEAT 68 75 3. FT REPEAT 76 83 4. FT REPEAT 84 91 5. FT IN PATIENTS WHO HAVE A PRP MUTATION AT FT CODON 178: PATIENTS WITH MET DEVELOP FFI, FT THOSE WITH VAL DEVELOP CJD). FT /FTId=VAR_006467. FT VARIANT 171 171 N -> S (IN SCHIZOAFFECTIVE DISORDER). FT /FTId=VAR_006468. FT VARIANT 178 178 D -> N (IN FFI AND CJD). FT /FTId=VAR_006469. FT VARIANT 180 180 V -> I (IN CJD). FT /FTId=VAR_006470. FT VARIANT 183 183 T -> A (IN FAMILIAL SPONGIFORM FT ENCEPHALOPATHY). FT /FTId=VAR_006471. FT VARIANT 187 187 H -> R (IN GSS). FT /FTId=VAR_008746. FT VARIANT 188 188 T -> K (IN EOAD; DEMENTIA ASSOCIATED TO FT PRION DISEASES). FT /FTId=VAR_008748. FT VARIANT 188 188 T -> R. FT /FTId=VAR_008747. FT VARIANT 196 196 E -> K (IN CJD). FT /FTId=VAR_008749. FT /FTId=VAR_006472. SQ SEQUENCE 253 AA; 27661 MW; 43DB596BAAA66484 CRC64; MANLGCWMLV LFVATWSDLG LCKKRPKPGG WNTGGSRYPG QGSPGGNRYP PQGGGGWGQP HGGGWGQPHG GGWGQPHGGG WGQPHGGGWG QGGGTHSQWN KPSKPKTNMK HMAGAAAAGA VVGGLGGYML GSAMSRPIIH FGSDYEDRYY RENMHRYPNQ VYYRPMDEYS NQNNFVHDCV NITIKQHTVT TTTKGENFTE TDVKMMERVV EQMCITQYER ESQAYYQRGS SMVLFSSPPV ILLISFLIFL IVG // CC -!- DISEASE: PRP IS FOUND IN HIGH QUANTITY IN THE CC BRAIN OF HUMANS AND ANIMALS INFECTED CC WITH NEURODEGENERATIVE DISEASES KNOWN AS CC TRANSMISSIBLE SPONGIFORM ENCEPHALOPATHIES OR PRION CC DISEASES,LIKE: CREUTZFELDT-JAKOB DISEASE (CJD), CC GERSTMANN-STRAUSSLER SYNDROME (GSS), FATAL CC FAMILIAL INSOMNIA (FFI) AND KURU IN HUMANS; CC SCRAPIE IN SHEEP AND GOAT; BOVINE SPONGIFORM CC ENCEPHALOPATHY (BSE) IN CATTLE; TRANSMISSIBLE CC MINK ENCEPHALOPATHY (TME); CHRONIC WASTING CC DISEASE (CWD) OF MULE DEER AND ELK; FELINE CC SPONGIFORM ENCEPHALOPATHY (FSE) IN CATS AND CC EXOTIC UNGULATE ENCEPHALOPATHY (EUE) IN CC NYALA AND GREATER KUDU. THE PRION DISEASES CC ILLUSTRATE THREE MANIFESTATIONS OF CNS CC DEGENERATION: (1) INFECTIOUS (2) CC SPORADIC AND (3) DOMINANTLY INHERITED FORMS. CC TME, CWD, BSE, FSE, EUE ARE ALL THOUGHT TO CC OCCUR AFTER CONSUMPTION OF PRION-INFECTED CC FOODSTUFFS. DR EMBL; M13667; AAA19664.1; -. DR EMBL; M13899; AAA60182.1; -. DR EMBL; D00015; BAA00011.1; -. DR PIR; A05017; A05017. DR PIR; A24173; A24173. DR PIR; S14078; S14078. DR PDB; 1E1G; 20-JUL-00. DR PDB; 1E1J; 20-JUL-00. DR PDB; 1E1P; 20-JUL-00. DR PDB; 1E1S; 21-JUL-00. DR PDB; 1E1U; 20-JUL-00. DR PDB; 1E1W; 20-JUL-00. DR MIM; 176640; -. DR MIM; 123400; -. DR MIM; 137440; -. DR MIM; 245300; -. DR MIM; 600072; -. DR MIM; 604920; -. DR InterPro; IPR000817; Prion. DR Pfam; PF00377; prion; 1. DR PRINTS; PR00341; PRION. DR SMART; SM00157; PRP; 1. DR PROSITE; PS00291; PRION_1; 1. DR PROSITE; PS00706; PRION_2; 1. KW Prion; Brain; Glycoprotein; GPI-anchor; Repeat; Signal; KW 3D-structure; Polymorphism; Disease mutation.

6. Methods for Data Integration –Combining data from multiple, autonomous data sources. TAMBIS –ontology driven mediation of querying EcoCyc –ontology driven schema for warehousing BioPAX –ontology defined interchange format. –More recently, ComparaGRID

7. The History of Ontologies Annotation –Gene Ontology Function, Process, Component –Systems Biology Ontology Describes terms in computational models.

8. ComparaGRID Roslin Newcastle Cambridge John Innes NCYC Manchester 6 Investigators 5 Researchers Commenced: 2003

9. ComparGRID’s Problem Domain

10. Many Model Organism Databases 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg gatcttaatt tttttaaatt attgatttgt 12481 aggagctatt tatatattct ggatacaagt tctttatcag atacacagtt tgtgactatt 12541 ttcttataag tctgtggttt ttatattaat gtttttattg atgactgttt tttacaattg 12601 tggttaagta tacatgacat aaaacggatt atcttaacca ttttaaaatg taaaattcga 12661 tggcattaag tacatccaca atattgtgca actatcacca ctatcatact ccaaaagggc 12721 atccaatacc cattaagctg tcactcccca atctcccatt ttcccacccc tgacaatcaa 12781 taacccattt tctgtctcta tggatttgcc tgttctggat attcatatta atagaatcaa

11. Data Models, Model Data

12. domain ontologydatabase Databases and Knowledge SequenceRecord Sequence S_hasIDS_hasSeqStrS_hasLength Molecule DNASequenceRepresentation Representation seqStringlengthid

13. The Fluxion Stack Raw data Raw data Pub service Trans service integrator query data AggregationSemanticsSyntax JDBC OWL

14. The difficulties The Cost of Integration –building ontologies is often hard The Cost of Managing Change –biological knowledge tends to undergo a lot of flux The Scalabilty of Expressive Ontologies.

15. Getting the Semantics Upfront Instead of annotating heterogenous data sources after the event, why not do so upfront? Originators of the data are likely to understand it best. Spreads the cost among those contributing.

16. CARMEN Code, Analysis, Repository and Modelling for e-Neuroscience www.carmen.org.uk www.carmen.org.uk Engineering and Physical Sciences Research Council

17. Consortium & Profile Stirling St. Andrews Newcastle York Sheffield Cambridge Imperial Plymouth Warwick Leicester Manchester £4M over 4 years 20 Investigators Commenced 1 st October 2006

18. Virtual Laboratory for Neurophysiology Enabling sharing and collaborative exploitation of data, analysis code and expertise that are not physically collocated

19. The need for clear metadata Most neurosciences data is relative simple in structure But often contextually complex Sometimes associated with behavioural features

20. Requirements for CARMEN ontology, so far Subject description Experimental Process Experimental Data Statistical analysis Services Derived Data

21. How do we represent… Laboratory Experiments In silico Analysis Derived data

22. Functional Genomics Experiment (FuGE) Model of common components in science investigations, such as materials, data, protocols, equipment and software. Provides a framework for capturing complete laboratory workflows, enabling the integration of pre-existing data formats.

23. Re-use CARMEN Brain anatomy BIRNLex, FMA Taxonomy NCBI Taxonomy Sample preparation sepCV

24. What we need – lab based CARMEN Age/stage development Subject preparation Subject training Subject task Experiment process Equipment Subject stimulus

25. What we need – In silico CARMEN File formats Data structures Statistics Algorithms Software

26. Align with OBI Aims to provide an ontology for the life sciences Consortium to 15 communities from crop science to neuroscience CARMEN will align and contribute to OBI Ontology for Biomedical Investigations

27. The Difficulties Even with a lot of pre-existing work there is a lot to describe OBI has 15 communities involved in it Bio-ImagingJeff GretheBiomedical Informatics Research Network (BIRN) Coordinating CenterUniversity of California, San DiegoWinter 2007 Daniel RubinRadiological Society of North America (RSNA)National Center for Biomedical Ontology at Stanford Medical Informatics and the Department of Radiology, Stanford UniversityWinter 2007 Bill BugBiomedical Informatics Research Network (BIRN)Laboratory of Bioimaging and Anatomical Informatics, in the Department of Neurobiology and Anatomy, Drexel University College of MedicineSpring 2006 Cellular AssaysStefan Wiemann DKFZ Clinical InvestigationsJennifer FostelClinical Trial OntologyNIEHS, National Institute for Environmental Health SciencesSpring 2004 Tina Hernandez-Boussard Department of Genetics, Stanford Medical SchoolFall 2007 Crop SciencesRichard BruskiewichGeneration Challenge ProgrammeIRRI ElectrophysiologyFrank GibsonCARMENSchool of Computing Science, Newcastle UniversitySpring 2007 Environmental OmicsNorman Morrison NERC Environmental Bioinformatic Centre and School of Computer Science, The University of ManchesterSpring 2004 Flow CytometryRyan BrinkmanISAC and FICCSBritish Columbia Cancer Research Center and University of British Columbia in the Department of Medical Genetics, Vancouver, BC, CanadaSpring 2004 Genomics/MetagenomicsDawn FieldGenome CatalogueNERC Centre for Ecology and HydrologyWinter 2005 Tanya GrayWinter 2005 ImmunologyRichard ScheuermannImmPort, FICCS, BioHealthBaseUniversity of Texas Southwestern Medical Center, in in Department of Pathology and Division of Biomedical InformaticsSpring 2006 Bjoern PetersImmune Epitope Database and Analysis ResourceLa Jolla Institute for Allergy and ImmunologySpring 2006 In Situ Hybridization and ImmunohistochemistryEric DeutschMISFISHIE MetabolomicsSusanna SansoneMSI, The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Daniel SchoberSpring 2006 NeuroinformaticsBill BugBiomedical Informatics Research Network (BIRN)Laboratory of Bioimaging and Anatomical Informatics, in the Department of Neurobiology and Anatomy, Drexel University College of MedicineSpring 2006 Frank GibsonCARMENSchool of Computing Science, Newcastle UniversitySpring 2007 NutrigenomicsPhilippe Rocca-SerraRSBIThe European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 PolymorphismTina Hernandez-BoussardPharmGKBDepartment of Genetics, Stanford Medical SchoolWinter 2006Fall 2007ProteomicsSusanna SansonePSIThe European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Daniel SchoberSpring 2006 Luisa MontecchiThe European Bioinformatics Institute EBI-EMBLSpring 2006 Chris Taylor Trish Whetzel Spring 2004 Frank GibsonSchool of Computing Science, Newcastle UniversitySpring 2007 ToxicogenomicsJennifer FostelToxicogenomicsNIEHS, National Institute for Environmental Health SciencesSpring 2004 Susanna SansoneRSBI The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 TranscriptomicsSusanna SansoneMGED The European Bioinformatics Institute EBI-EMBL, NET ProjectSpring 2004 Philippe Rocca-SerraSpring 2004 Trish Whetzel Spring 2004 Chris StoeckertDepartment of Genetics and Center for Bioinformatics, University of PennsylvaniaSpring 2004 Gilberto FragosoNCI Center for BioinformaticsSpring 2004 Joe White Helen ParkinsonThe European Bioinformatics Institute EBI-EMBLSpring 2004 Mervi Heiskanen Liju FanOntology Workshop, LLC, Columbia, MD, USASpring 2004 Helen CaustonImperial CollegeSpring 2004

28. Information Extraction More semantics is better? How do we get extract the information? http://en.wikipedia.org/wiki/Image:Brain_090407.jpg

29. Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN)

30. Robot Reference set of 5,000 mutant strains ‘Folate’ +-+- ‘MMS’ --++ Data curation. Functional analysis. Interactions with in silico programme. * * * Robot Screen mutants for sensitivity to damage/nutrition Identification of novel interactions between nutrition and damage using automated yeast screening and analysis

31. CISBAN dataflow

32. Data Entry with SYMBA http://symba.sourceforge.net/

33. Data Entry with SYMBA

34. CARMEN and CISBAN We can provide more semantics upfront This should make data more explicit If we still need to integrate it should be easier. Like much of biology, these projects are largely using structural simple, non-SW based technologies. This is a lot of effort to go to; what do we hope to gain?

35. Yeast Hub YeastHub: a semantic web use case for integrating data in the life sciences domain Kei-Hoi Cheung, Kevin Y. Yip, Andrew Smith, Remko deKnikker, Andy Masiar and Mark Gerstein doi:10.1093/bioinformatics/bti1026

36. A rapturous reception So the general idea is take a bunch of data, convert it to RDF, dump it into a RDF triple store […] to discover interesting things ? –http://www.nodalpoint.org/user/greghttp://www.nodalpoint.org/user/greg Putting a lot of RDF in a bucket isn’t integration. Not unless the RDF is the same schema and using the same concepts –Carole Goble, University of Manchester

37. A thin layer of semantics. Inverse Document Frequency is a method for classifying documents; rare words carry more information than common ones. In this case, YeastHub has a common semantics describing the type of document. “protein” or “sequence” occurs a lot in Uniprot, but less in the bulk corpus Rather than treating all documents equally, they use IDF twice. Leveraging Biological Identifier Relationships and Related Documents to Enhance Information Retrieval for Proteomics -- Smith et al., 10.1093/bioinformatics/btm452 – BioinformaticsLeveraging Biological Identifier Relationships and Related Documents to Enhance Information Retrieval for Proteomics -- Smith et al., 10.1093/bioinformatics/btm452 – Bioinformatics

38. Thin Semantics The semantics of YeastHub is not deep. But even a thin layer of semantics is useful. If we modify our technologies to use it. A large part of library sciences has been encoded in 15 tags – Dublin Core

39. Using Ontology to Classify Members of a Protein Family Katy Wolstencroft (Bioinformatics) Daniele Turi (Instance Store) Phil Lord (myGrid) Lydia Tabernero (Protein Scientist) Matt Horridge, Nick Drummond et al (Protégé OWL) Andy Brass and Robert Stevens (Bioinformatics)

40. The Protein Phosphatases A large superfamily of proteins Motifs determine a protein’s place within the family Recognising that motifs imply class membership is normally manual Can these be captured in an ontology?

41. Phosphatase Functional Domains Andersen et al (2001) Mol. Cell. Biol. 21 7117-36

42. Definition of Tyrosine Phosphatase Class TyrosineRreceptorProteinPhosphatase EquivalentTo: Protein That -(contains atLeast-1 ProteinTyrosinePhosphataseDomain and -contains 1 TransmembraneDomain

43. Classifying Proteins >uniprot|Q15262|PTPK_HUMAN Receptor-type protein-tyrosine phosphatase kappa precursor (EC 3.1.3.48) (R-PTP-kappa). MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQDLYDDFEWVHV SAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKENDTHCIDFSYLLYSQKGLNP GTLNILVRVNKGPLANPIWNVTGFTGRDWLRAELAVSSFWPNEYQVIFEAEVSGGRSGYI AIDDIQVLSYPCDKSPHFLRLGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPV……….. InterPro Instance Store Reasoner Translate Codify

44. Results Human phosphatases have been classified using the system The ontology system refined classification - DUSC contains zinc finger domain characterised and conserved – but not in classification - DUSA contains a disintegrin domain previously uncharacterised – evolutionarily conserved We have automated a part of the scientific process –We have defined our domain model in a computational form –We have collected some data –We have let the reasoner test whether the model fits the data The semantics here are deeper with YeastHub, which allow us to reason

45. OGRE Like ComparaGRID dealing with comparing genomes from different species. In this case concerned with the automated pairwise comparisons of bacterial genomes. Genome change and evolution is often key to pathogenisis and resistance in bacteria.

46. OGRE Unlike phosphotase, the raw analysis results are probabalistic. Classification in the same way is therefore not possible.

47. (3) Two regions of similarity picked for analysis. Semantic matching and mediation are used to match input data to Bayesian network inputs #Similarity Hit 2 #Similarity Hit 1 #Gap #End Integer (1) Raw similarity data (e.g. Blast report)‏ Annotation of similarity data 5kb10kb0kb 5kb10kb0kb Source Target a a'b' b c c' b - (#start 3.5kb, #end 5.5kb), (#start 3.5kb, #end 5.5kb), #similarity 85% c - (#start 9.5kb #end 10kb), (#start 6kb, #end 6.5kb), #similarity 95% a - (#start 0kb, #end 2kb), (#start 2kb, #end 0kb), #similarity 90% #Insertion #Deletion #Start Integer #End Integer #Start Integer #End Integer #Score Double #Start Integer #End Integer #Start Integer #Score Double #Start Integer #End Integer #Start Integer 5kb10kb0kb Source Target a a'b' b c c' OGRE Analysis Architecture

48. OGRE It is possible to combine statistical and logical reasoning Doing so architecturally allows reasoning that is hard with either alone.

49. Summary Ontologies have been used in life sciences for data integration Increasingly, are being used to describe the data early in the scientific process Even thin semantics can be exploited for information retrieval Richer semantics allows more use of computational inference

50. Richer Expressivity There are applications of more expressive semantics Can we move to from specific software, to generic software with specific knowledge models But, scalability and usabilityremain the bottleneck

51. Industrialisation Semantics in the life sciences is moving from small to large scale –building ontologies has now become very committee driven –we don’t understand ontology engineering as we do software engineering –Encapsulation, modularisation, continuous integration.

52. Future ComparaGRID has semantics describing schema which means data integration can happen on-the-fly. Death to data warehouses! CARMEN and CISBAN are gathering semantically enriched data in the first place. An End to Integration! Semantics during dissemination Knowledge for All.

53. The Future An End to Integration, Death to Warehouses Semantics during dissemination Knowledge for All

54. Acknowledgements The ComparaGRID consortium is Madhuchhanda Bhattacharjee, Richard Boys, Tony Burdett, Rob Davey, Jo Dicks, David Marshall, Andy Law, Phillip Lord, Trevor Paterson, Matthew Pocock, Peter Rice, Ian Roberts, Robert Steven, Paul Watson, Darren Wilkinson and Neil Wipat, Andy Gibson CISBAN is Tom Kirkwood (PI), Thomas von Zglinicki (PI), David Lydall (PI), Anil Wipat (PI), Stephen Addinall (Research Associate), Suzanne Advani (Technician), Kim Clugston (Research Associate), Sharon Denley (PA to Professor Tom Kirkwood), Amanda Greenall (Research Associate), Jennifer Hallinan (Research Associate), Dominic Kurian (Research Associate), Conor Lawless (Research Associate), Guiyuan Lei (Research Associate), Allyson Lister (Research Associate), Mandy Maddick (Research Associate), Satomi Miwa (Research Associate), Glyn Nelson (Research Associate), Bob Nicholson (Superintendent), Sharon Oljslagers (Technician), Joao Passos (Research Associate), Carole Proctor (Research Associate), Daryl Shanley (Research Associate), Oliver Shaw (Research Associate), Donna Stark (Research Secretary), Laura Steedman (Technician), Joyce Wang (Technician), Darren Wilkinson (Professor of Stochastic Modelling)

55. CARMEN Acknowledgements Professor Colin Ingram, Professor Jim Austin, Professor Leslie Smith, Professor Paul Watson Dr. Stuart Baker,Professor Roman Borisyuk, Dr. Stephen Eglen, Professor Jianfeng Feng, Dr. Kevin Gurney, Dr. Tom Jackson Dr. Marcus Kaiser, Dr. Phillip Lord, Dr. Paul Overton, Dr. Stefano Panzeri, Dr. Rodrigio Quian Quiroga, Dr. Simon Schultz, Dr. Evelyne Sernagor, Dr. V. Anne Smith, Dr. Tom Smulders Professor Miles Whittington, Christoph Echtermeyer, Martyn Fletcher, Frank Gibson, Mark Jessop Dr. Bojian Liang, Juan Martinez-Gomez, Dr. Chris Mountford, Agah Ogungboye, Georgios Pitsilis, Dr. Daniel Swan University of St Andrews The University Of Sheffield

56. Holiday Pictures

57. Questions?