1. The document discusses how a biologist, Marco Roos, became interested in e-science through his work in molecular and cellular biology, bioinformatics, and data integration projects. 2. Roos describes how e-science allows for collaboration between different experts and disciplines through technologies like workflows, semantic web, and virtual laboratories. 3. Roos emphasizes that e-science should empower scientists by making tools and resources easy to use, share, and build upon so that scientists can focus on scientific problems rather than technical challenges.

1. A biologists in e-Science? by Marco Roos Acknowledgements: Scott Marshall, Edgar Meij, Sophia Katrenko, Willem van Hage, Pieter Adriaans, Martijn Schuemie, Carole Goble, Dave de Roure, Katy Wolstencroft, Andy Gibson, the myGrid and myExperiment teams, many others who share their ideas, and… You! * Project or Area Liaison for OMII-UK (domain: Biology and Bioinformatics) BioAssist programmers meeting November 17, 2008, Utrecht, The Netherlands

3. Introducing myself A biologist

4. My prime interest Structure and function of DNA in the nucleus Escherichia coli Mouse fibroblast (skin) cells

8. My prime interest Structure and function of DNA in the nucleus Escherichia coli Mouse fibroblast (skin) cells

9. Components controlling structure & function of DNA

10. Connecting the dots (example: protein interaction network in yeast)

11. Biomedical knowledge repository PubMed statistics http://www.ncbi.nlm.nih.gov/entrez >17 million citations >400,000 added/year ~70,000 searches/month … Does not compute Does not fit

13. What do I do? A needy biologist

14. ‘ Old school’ Bioinformatics A typical bioinformatician

15. ‘ Old school’ Bioinformatics A biologist behind a computer who (just) learned perl

16. /* * determines ridges in htm expression table */ #include &quot;ridge.h&quot; int selecthtm(PGconn *conn, char *htmtablename, char *chromname, PGresult *htmtable) { char querystring[256]; sprintf(&quot;SELECT * FROM %s WHERE chrom = %s ORDER BY genstart&quot;, htmtablename, chromname); htmtable = PQexec(conn, querystring); return(validquery(htmtable, querystring)); } int is_ridge(PGresult *htmtable, int row, double exprthreshold, int mincount) /* determines if mincount genes in a row are (part of) a ridge */ /* pre: htmtable is valid and sorted on genStart (ascending) /* post: { if (mincount<=0) return TRUE; if (row>=PQntuples(htmtable)) return FALSE; if(PQgetvalue(htmtable, 0, PQfnumber(htmtable, &quot;movmed39expr&quot;)) < exprthreshold) { return FALSE; } return(is_ridge(htmtable, ++row, exprthreshold, --mincount)); } int main() { PGconn *conn; /* holds database connection */ char querystring[256]; /* query string */ PGresult *result; int i; conn = PQconnectdb(&quot;dbname=htm port=6400 user=mroos password=geheim&quot;); if (PQstatus(conn)==CONNECTION_BAD) { fprintf(stderr, &quot;connection to database failed.&quot;); fprintf(stderr, &quot;%s&quot;, PQerrorMessage(conn)); exit(1); } else printf(&quot;Connection ok&quot;); sprintf(querystring, &quot;SELECT * FROM chromosomes&quot;); printf(&quot;%s&quot;, querystring); result = PQexec(conn, querystring); if (validquery(result, querystring)) { printresults(result); } else { PQclear(result); PQfinish(conn); return FALSE; } PQclear(result); PQfinish(conn); return TRUE; } int printresults(PGresult *tuples) { int i; for (i=0; i< PQntuples(tuples) && i < 10; i++) { printf(&quot;%d, &quot;, i); printf(&quot;%s&quot;, PQgetvalue(tuples,i,0)); } return TRUE; } int validquery(PGresult *result, char *querystring) { printf(&quot; in validquery&quot;); if (PQresultStatus(result) != PGRES_TUPLES_OK) { printf(&quot;Query %s failed.&quot;, querystring); fprintf(stderr, &quot;Query %s failed.&quot;, querystring); return FALSE; } return TRUE; }

17. Theme Not an e-Science approach

18. The ‘spaghetti’ approach

19. Computational tools graveyard rephrasing David Shotton

20. Database survival: <20% ‘no problems’

21. Data graveyard quoting David Shotton

24. Empowering biologists and bioinformaticians

26. Experiment 1: Model based data integration Example: UCSC genome browser partOf * * Transcription Factor Binding Site

29. Which diseases are associated with my protein of interest ‘EZH2’

30. Biological knowledge extraction Biological question/model Computational experiment Extracted knowledge >17 million citations +400,000/yr

31. Combining expertise Edgar Meij Information retrieval expert

32. Combining expertise Sophia Katrenko Machine learning expert

33. Combining expertise Willem van Hage Semantic web expert (and bass guitar player)

34. Combining expertise Towards a knowledge framework Computer scientist and bioinformatician Scott Marshall

35. The AIDA toolbox, Web Services for knowledge extraction and knowledge management

36. e -Science collaboration AIDA toolbox

37. “ Collaboration through Web Services” Bio-text mining expert BioSemantics group, Erasmus University Rotterdam Martijn Schuemie

38. “ Collaboration through Web Services” Biological Database expert Hideaki Sugawara

39. “ Collaboration through Web Services” e -bioscientist

40. A nice experiment design

41. A not so nice experiment design

42. A workflow Protocol for a computational experiment

43. 05/06/09 BioAID

44. 05/06/09 BioAID

45. Sharing and publishing my designs

46. Bio AID Disease Discovery workflow 05/06/09 BioAID AIDA AIDA OMIM service (Japan) AIDA ‘ Taverna shim’ Taverna ‘shim’

47. Bio AID Disease discovery workflow 05/06/09 BioAID

48. Bio AID Disease discovery workflow 05/06/09 BioAID

49. An insightful computational experiment

50. e -Science leveraging the use of more brains Want this…

51. e -Science leveraging the use of more brains … need this

52. Publish and share Publish & share research objects myExperiment >400 workflows >1000 registered users (< 1yr) Run workflows without Taverna (expert feature) Open to objects other than workflows Link out to other resources

53. Do I feel all powerful now? An e -biologist?

55. Underestimated: The brain bottleneck

56. Empower me with a ‘virtual brain’ * From P.J. Verschure, Journal of Cellular Biochemistry 2006, vol. 99(1), pg 23-34 My ws Your ws My ws Your ws My ws *

57. Workflow and Semantic Web Query Retrieve documents from Medline Extract proteins ( Homo sapiens ) Calculate ranking scores Create biological cross references Convert to table (html) Add documents (IDs) to semantic model Add proteins to semantic model Add scores to semantic model Add cross references to semantic model Add query to semantic model

58. Do I feel all powerful now? An e -biologist?

59. http://staff.science.uva.nl/~roos/ChromatinWorkgroup/

60. e -Laboratory factories

61. Conclusions How do we know when e-Science has succeeded? Not just accelerated but new A. When everyone is using Grid computing? B. When scientists make scientific advances that would not have happened otherwise? Slide from ‘The New e-Science’ by Dave de Roure

65. How many brains do you want to use? – One?

66. Some?

67. Many?

68. Use your community myGrid/myExperiment OMII-UK You