BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
ANNEX C
PP1
BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL
SCIENCE MERIT PROMOTION PROCEDURE
PROMOTION PROPOSAL FORM
(covering promotions from Postdoctoral Research Scientist to Senior Postdoctoral Research Scientist
and from Senior Postdoctoral Research Scientist to Band 4) under the BBSRC Science Merit
Promotion Procedure
1.
INSTITUTE Biomathematics & Statistics Scotland (BioSS) ....................................
2.
CANDIDATE'S NAME Husmeier, Dirk ....................................................................
(surname first)
3.
PRESENT\PROPOSED GRADES Band 5/Band 4 ................................................
4.
PROPOSAL wef 1 July 2006 ...........
5.
QUALIFICATIONS AND OTHER PARTICULARS RELEVANT TO THE
PROPOSAL:
(State recognised qualifications, with subjects and dates attained; for degrees, state class and
where obtained. Give membership of learned and professional societies)
(i) Qualifications etc. gained prior to entry to present grade



Diplom-Physiker (1st class honours), University of Bochum (Germany), 1991
M.Sc. (distinction), Information Processing and Neural Networks, King’s College
London, 1994
Ph.D. Applied Mathematics and Neural Computation, King’s College London, 1997
(ii) Qualifications etc. gained since entry to present grade



Member of the International Society for Computational Biology (ISCB)
Honorary affiliation with the School of Informatics at Edinburgh University and
approved first PhD supervisor
Honorary affiliation with the Scottish Centre for Genome Technology and Informatics
(GTI)
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ANNEX C - CONTD
6.
CAREER HISTORY:
(since finishing full-time education)



7.
Postdoctoral researcher, 1997-1999, Imperial College London
Postdoctoral researcher, 1999-2002, Biomathematics & Statistics Scotland
Bioinformatics researcher (Band 5), 2002-present, Biomathematics & Statistics
Scotland
PREVIOUS PROPOSALS OR APPEALS AT THIS LEVEL IN THE PAST 3 YEARS:
None ..............................................................................
8.
ORGANISATION CHART:
(To be completed for all candidates). Provide a family tree, showing the candidate's position
in the Division (or equivalent). Identify the numbers and grades of employees managed and
all significant internal & external working relationships.
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ANNEX C - CONTD
9.
DESCRIPTION OF PRESENT DUTIES:
Please list the postholder’s main duties. Percentage time spent on each main activity must be
shown.
DUTIES
%

SEERAD core funded research
70
Methodological research in bioinformatics and computational biology. This work involves a
collaboration with the Scottish Centre for Genome Technology and Informatics (GTI), the
supervision of PhD and MSc students in the School of Informatics at Edinburgh University,
and leading the bioinformatics research within BioSS.

SEERAD flexible fund project
20
Research on systems biology of host-pathogen interactions in collaboration with SCRI and
MRI. This project involves managing a postdoctoral research assistant.

Collaboration with and support for SABRI/SAC scientists in computational biology
10
Phylogenetic analysis of mitochondrial DNA sequences for molecular biologists at SCRI.
ANNEX C - CONTD
10.
CANDIDATE'S CONTRIBUTION TO WORK:
(This should be discussed with the candidate). Indicate the proportion of the candidate's total
effort under each of the following headings
%
(i) Scientific innovation
70
(ii) Technical innovation
20
(iii) Scientific and\or technical support
5
(iv) Management of employees
5
(v) Other duties (specify briefly)
11.
---
PUBLICATIONS:
(a) Scientific papers - List in chronological order, giving the author(s), year, title of article,
journal, volume no. and first and last page nos. For multi-author refereed papers, give an
indication of the %age contribution by the candidate to (i) initiation, conception and
planning of the work, (ii) its execution and (iii) the writing of the paper.
For ease of reference, number each publication listed. Those to be presented as the
candidate's six most significant papers should be marked with an asterisk (see also 11b).
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(i)
%
1.
Schlitter J., Husmeier D. (1992):
"System Relaxation and Thermodynamic Integration",
Molecular Simulation 8, 285-295.
(ii)
%
(iii)
%
[50,70,30]
2.
Steinhoff H.J., Schlitter J., Redhardt A., Husmeier D., Zander N. (1992):
"Structural fluctuations and conformational entropy in proteins: entropy balance in an intramolecular
reaction in methemoglobin",
Biochimica et Biophysica Acta 1121, 189-198.
[20,40,10]
3.
Husmeier D., Taylor J.G. (1997):
"Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network."
In: Smith G.D., Steele N.C., Albrecht R.F. (Eds.): Artificial Neural Networks and Genetic Algorithms,
477-481, Springer Verlag, ISBN 3-211-83087-1.
[90,100,95]
4.
Husmeier D., Taylor J.G. (1997):
" Modelling Conditional Probabilities with Committees of RVFL Networks "
In: Gerstner W., Germond A., Hasler M., Nicoud J.D. (eds.): International Conference on Artificial
Neural Networks - ICANN '97 , Lecture Notes in Computer Science 1327, Springer Verlag (ISBN 3-54063631-5), 1053-1058.
[90,100,95]
5.
Husmeier D., Allen D., Taylor J.G. (1997):
"A Universal Approximator for Learning Conditional Probability Densities",
in Ellacott S.W., Mason J.C., Anderson I.J. (eds.): Mathematics of Neural Networks: Models, Algorithms,
and Applications, Kluwer Academic Press, Boston (ISBN: 0-7923-9933-1), 198-203.
[90,100,95]
6.
Husmeier D., Taylor J.G. (1997):
"Predicting Conditional Probability Densities of Stationary Stochastic Time Series",
Neural Networks 10 (3), 479-497.
7.
Husmeier D., Taylor J.G. (1998):
[90,100,95]
"Neural Network for Predicting Conditional Probability Densities: Improved Training Scheme
Combining EM and RVFL ",
Neural Networks 11 (1), 89-116.
8.
Husmeier D., Penny W.D., Roberts S.J. (1998):
[80,100,95]
"Empirical Evaluation of Bayesian Sampling for Neural Classifiers" .
In: Niklasson L., Boden M., Ziemke T. (eds.): International Conference on Artificial Neural Networks ICANN '98 , Perspectives in Neural Computing, Springer Verlag (ISBN 3-540-76263-9), 323-328.
9.
Husmeier D., Althoefer K. (1998):
"Modelling conditional probabilities with network committees: how overfitting can be useful" ,
Neural Network World 8 (4), 417-439.
[90,100,100]
*
10. Roberts S.J., Husmeier D., Rezek I., Penny W. (1998):
"Bayesian Approaches to Gaussian Mixture Modeling",
IEEE Transactions on Pattern Analysis and Machine Learning 20 (11), 1133-1142.
[90,100,95]
[20,30,10]
11. Penny W.D., Husmeier D., Roberts S.J. (1999):
"The Bayesian Paradigm: Second Generation Neural Computing."
In: Lisboa P.J.G., Ifeachor E.C., Srczepaniak A.S. (Eds.), Artificial Neural Networks in Biomedicine,
Springer (ISBN: 1-85233-005-8), 11-23.
[30,30,20]
12. Penny W.D., Husmeier D., Roberts S.J. (1999):
"Covariance-based weighting for optimal combination of network predictions".
In: Willshaw, D. and Murray, A. (Eds.): Proceedings of the International Conference on Artificial Neural
Networks (ICANN99), IEE Press, Edinburgh, 826-831.
[20,0,10]
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BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
13. Husmeier D., Roberts S.J. (1999):
"Regularisation of RBF-Networks with the Bayesian Evidence Scheme".
In: Willshaw, D. and Murray, A. (Eds.): Proceedings of the International Conference on Artificial Neural
Networks (ICANN99), IEE Press, Edinburgh, 533-538.
[90,100,95]
14. Husmeier D., Penny W., Roberts S.J. (1999):
"An Empirical Evaluation of Bayesian Sampling with Hybrid Monte Carlo for Training Neural Network
Classifiers" ,
Neural Networks 12, 677-705.
[90,100,95]
15. Husmeier D. (2000):
"Bayesian Regularization of Hidden Markov Models with an Application to Bioinformatics",
Neural Network World 10 (4), 589-595.
[100,100,100]
16. Husmeier D. (2000):
"Learning Non-Stationary Conditional Probability Distributions" ,
Neural Networks 13, 287-290.
[100,100,100]
*
17. Husmeier D. (2000):
"The Bayesian Evidence Scheme for Regularising Probability-Density Estimating Neural Networks" ,
Neural Computation 12 (11), 2685-2717.
[100,100,100]
18. Husmeier D., Wright F. (2000):
"Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models" .
In Bornberg-Bauer E., Rost U., Stoye J., Vingron M. (eds.): 15th German Conference on Bioinformatics
(GCB 2000), Logos Verlag Berlin (ISBN 3-89722-498-4), 19-26.
[50,100,90]
19. Husmeier D., Wright F. (2001):
"Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures".
In Wingender E., Hofestaedt R., Liebich I. (eds.): 16th German Conference on Bioinformatics (GCB
2001). ISBN 3-00-008114-3, 182-184.
[50,100,90]
20. Althoefer K., Krekelberg B., Husmeier D., Seneviratne L. (2001):
"Reinforcement learning in a rule-based navigator for robotic manipulators",
Neurocomputing 37 (1-4), 51-70.
[25,10,0]
21. Husmeier D., Wright F. (2001):
"Probabilistic Divergence Measures for Detecting Interspecies Recombination" ,
Bioinformatics 17, Suppl. 1, S123-S131. Also presented at ISMB 2001.
[50,100,90]
*
22. Husmeier D., Wright F. (2001):
"Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models",
Journal of Computational Biology 8 (4), 401-427.
[50,100,90]
23. Husmeier D., Wright F. (2002):
"A Bayesian Approach to Discriminate between Alternative DNA Sequence Segmentations",
Bioinformatics 18 (2), 226-234.
[70,100,90]
24. Husmeier D., McGuire G. (2002):
"Detecting recombination with MCMC" ,
Bioinformatics 18: S345-S353. Also presented at ISMB 2002.
[50,50,100]
*
25. Husmeier D., McGuire G. (2003):
"Detecting Recombination in 4-Taxa DNA Sequence Alignments with Bayesian Hidden Markov Models
and Markov Chain Monte Carlo" ,
Molecular Biology and Evolution 20(3):315-337
[50,50,100]
26. Husmeier D. (2003)
”Reverse engineering of genetic networks with Bayesian networks”
Biochemical Society Transactions 31 (6): 1516-1518.
Last updated by nt 4/02
[100,100,100]
AMDT 182 – APR 2002
BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
*
27. Husmeier D. (2003)
"Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with
dynamic Bayesian networks"
Bioinformatics 19: 2271-2282.
[100,100,100]
28. Milne I., Wright F., Rowe G., Marshall D.F., Husmeier D., McGuire G. (2004)
"TOPALi: software for automatic identification of recombinant sequences within DNA multiple
alignments"
Bioinformatics 20: 1806-1807
[20,20,0]
29. Husmeier D., Wright F., Milne I. (2005)
”Detecting interspecific recombination with a pruned probabilistic divergence measure”
Bioinformatics 21(9):1797-1806
[90,90,100]
*
30. Husmeier D. (2005)
”Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments
with phylogenetic factorial hidden Markov models “
Bioinformatics 21: ii166-ii172. Also presented at ECCB 05.
[100,100,100]
ANNEX C - CONTD
11.
PUBLICATIONS (CONTD.):
(i)
(ii)
(iii)
(a)
%
%
%
Scientific papers (Contd.)
Submitted
31. Lehrach W., Husmeier D., Williams C.W.K (2005)
A regularised discriminative model for the prediction of protein-peptide interactions.
Submited to Bioinformatics.
[30,20,50]
32. Lehrach W., Husmeier D., Williams C.W.K (2005)
Probabilistic in silico prediction of protein-peptide ineractions
Accepted for publication in Lecture Notes in Computational Biology, Springer Verlag .
[30,20,50]
ANNEX C - CONTD
11.
PUBLICATIONS (CONTD.)
(b) Other Publications - list under the following headings:
(i)
(ii)
(iii)
(iv)
(v)
(vi)
Proceedings of societies
Review Articles
Books
Technical Reports & Bulletins
Popular articles
Other
For ease of reference, number each publication listed. Those to be presented as the candidate's
six most significant papers should be marked with an asterisk (see also 11a).
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(i) Proceedings of Societies
33. Husmeier D., Taylor J.G. (1996):
"A Neural Network Approach to Predicting Noisy Time Series",
in Ludermir T.B. (ed.): Annals of the Third Brazilian Symposium on Neural Networks, 221-226, Recife,
Brazil, 1996.
34. Husmeier D., Patton G.S., McClure M.O., Harris J.R.W., Roberts S.J.(1999):
"Neural Networks for Predicting Kaposi's Sarcoma" .
In: Boswell, J.S. (Ed.): Proceedings of the International Joint Conference on Neural Networks
(IJCNN99), ISBN 0-7803-5532-6
35. Husmeier D., Wright F. (2001):
Detecting past recombination events in Potato virus Y genomic sequences using statistical methods.
Scottish Crop Research Institute, Annual Report 2000/2001 , 158-162.
ISBN 0 9058 75176
36. Husmeier D. (2002)
Contribution to the discussion on statistical modelling and analysis of genetic data
Journal of the Royal Statistical Society B, 64 (4), 751
37. Husmeier D. (2003)
Inferrring gene interactions with Bayesian networks
In Spang R., Beziat P., Vingron M. (eds.): Currents in Computational Molecular Biology, poster
proceedings of RECOMB 2003, 303-304.
38. Husmeier D. (2003)
Statistical Methods for Detecting Recombination in DNA Sequence Alignments. Proceedings of the 49th
German Biometrics Conference, page 16. Available online at http://www.biometrie2003.unwuppertal.de/programm/e_programm.asp#Bioinformatik
39. Husmeier D. (2004)
Pruned PDM Method for Detecting Recombination
In Apostol Granada and Philip E. Bourne (eds.): Currents in Computational Molecular Biology, poster
proceedings of RECOMB 2004, pp.264-265.
40. Glasbey C. and Husmeier D. (2004)
Contribution to the discussion on the paper by Friedman and Meulman
Journal of the Royal Statistical Society B, 66 (4), 840-841
41. Lehrach W., Husmeier D., Williams C., Barber D. (2004)
Using TDNNs to Predict Protein Interactions by Locating Relevant Sequence Features
ISMB 2004 , poster proceedings.
42. Husmeier D., Wright F., Milne I. (2004)
Pruning the probabilistic divergence measure for improved detection of interspecific recombination
ISMB 2004 , poster proceedings.
43. Husmeier D. (2005)
Phylogenetic Factorial Hidden Markov Models for Detecting Mosaic Structures in DNA Sequence
Alignments
ISMB 2005, poster proceedings, available online at http://www.iscb.org/ismb2005/ .
44. Husmeier D. ((2005)
Learning local gene interaction networks from noisy expression data with probabilistic graphical models
the probabilistic divergence measure for improved detection of interspecific recombination
In Plotkin G. (Ed.): Third International Workshop on Computational Methods in Systems biology, p.1
45. Lehrach W., Husmeier D., Williams C. (2005)
A Regularised Discriminative Model for the Prediction of Protein-Peptide Interactions
ECCB 2005, poster proceedings, available online at http://www.eccb05.org/ .
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46. Husmeier D. (2005)
Detecting Mosaic Structures in DNA Sequence Alignments. Workshop on Statistics in Genomics and
Proteomics. Book of Abstracts, edited by Lisete Sousa and Luzia Goncalves. ISBN 972-8859-35-X,
page15. Available online at http://wsgp.deio.fc.ul.pt/Abstracts%20Keynote/dirk_husmeier.html .
(ii) Review Articles
None
(iii) Books
47. Husmeier D. (1999)
Neural Networks for Conditional Probability Estimation
Springer Verlag, London
48. Husmeier D., Dybowski R., and Roberts S. (2005)
Probabilistic Modeling in Bioinformatics and Medical Informatics
Springer Verlag, New York
(iv) Technical Reports
49. Husmeier D., McGuire G. (2002):
Detecting Recombination in DNA Sequence Alignments: A Comparison between BARCE and RECPARS.
Available online at: http://www.bioss.sari.ac.uk/~dirk/software/BARCEtdh/Manual/simuMBE/main.html
50. Werhli A., Grzegorczyk, M., Chiang M., Husmeier D. (2005)
Improved Gibbs sampling for detecting recombination in DNA sequence alignments.
Technical report, to be published by CIM (http://www.cim.pt/ ).
(v) Popular Articles
51. Husmeier D. (2001)
Paradigm Shifts in Information Technology
Essay, available online at http://www.bioss.sari.ac.uk/~dirk/index_essays.html
(vi) Other (Software)
52. Husmeier D. (2001)
JAMBE: Java software package to detect recombination in DNA sequence alignments. Available online
at http://www.bioss.sari.ac.uk/~dirk/software/Jambe/Manual/INFO.html and integrated into TOPALi,
available at http://www.bioss.ac.uk/%7Eiainm/topali/ .
53. Husmeier D. .(2002)
SIRENS: SImulating REcombination in Nucleotide Sequences.
MATLAB program for simulating recombination in DNA sequence alignments of four sequences,
available online at http://www.bioss.sari.ac.uk/~dirk/software/Sirens/INFO.html .
54. McGuire G. and Husmeier D. (2002)
BARCE: Bayesian Application for Recombination and Gene Conversion. C++ program package for
detecting recombination breakpoints in four-sequence alignments using hidden Markov models.
Available online at http://www.bioss.sari.ac.uk/~dirk/software/BARCEtdh/Manual/INFO.html and integrated
into TOPALi, available at http://www.bioss.ac.uk/%7Eiainm/topali/ .
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55. Husmeier D. (2003)
DBmcmc: Software package written in MATLAB for inferring dynamic Bayesian networks with MCMC,
available online at http://www.bioss.sari.ac.uk/~dirk/software/DBmcmc/ .
56. Husmeier D. (2005)
BARGE: MATLAB programs for discriminating between recombination and reate heterogeneity in DNA
sequence
alignments
with
factorial
hidden
Markov
models;
available
online
at
http://www.bioss.sari.ac.uk/~dirk/Supplements/phyloFHMM/ .
(vi) Other (Selection of invited talks and lectures; a full list is available from http://www.bioss.sari.ac.uk/~dirk/My_talks.html )
57. Husmeier D. (1999)
Bayesian Methods for Neural Network Classifiers
Max-Planck Institute fuer Stroemungsforschung, 5 February 1999.
58. Husmeier D. (1999)
Regularisation of Mixture Models with the Bayesian Evidence Scheme
Gatsby Computational Neuroscience Unit, University College London, 16 April 1999.
59. Husmeier D. (2001)
Introduction to hidden Markov models
Application of hidden Markov models in Bioinformatics
Introduction to phylogenetics
Three invited lectures to the Statistics Department of the University of Dortmund (Germany), 9/10
January 2001
60. Husmeier D. (2001)
Statistical methods for detecting sporadic recombination in DNA sequence alignments
Talk held at the BBSRC grantholders' workshop in Hinxton, 21-23 February 2001, and at the workshop
on Mathematical and Statistical Aspects of Molecular Biology, Isaac Newton Institute for Mathematical
Sciences, Cambridge, 20-21 March 2001
61. Husmeier D. (2001)
Detection and selection of DNA mosaic structures.
Invited seminar. Department of Biomolecular Sciences, UMIST Manchester, June 2001
62. Husmeier D. (2001)
Statistical methods for detecting recombination and gene conversion in DNA sequence alignments.
Invited talk held at the Statistics Department of the University of Glasgow, 13 December 2001.
63. Husmeier D. (2002)
Introduction to statistical bioinformatics
Invited lectures held at the Statistics Department of Dortmund University, January 2002
64. Husmeier D. (2002)
Statistical methods for phylogenetic inference and the detection of recombination
Invited talk to the Annual Meeting of the Scottish and Northumbrian Academic Statisticians
May 2002
65. Husmeier D. (2002)
A short course on statistical bioinformatics
Invited lectures to the Regenstrief Institute, Indianapolis (USA), July 2002
66. Husmeier D. (2002)
Statistical methods for detecting recombination in DNA sequence alignments
Invited lecture held at the International School on Computational Biology, Le Havre, October 2002
67. Husmeier D. (2003)
Introduction to statistical phylogenetics
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Invited lecture to M.Sc. students in the Division of Informatics, University of Edinburgh, February 2003
68. Husmeier D. (2003)
Statistical Methods for Detecting Recombination in DNA Sequence Alignments
Invited talk given at the 49th German Biometric Conference, Wuppertal, March 2003
69. Husmeier D. (2003)
Introduction to Bayesian networks and the reverse engineering of genetic networks
Invited lecture ,University of Dortmund, Statistics Department, July 2003
70. Husmeier D. (2003)
Can we infer genetic networks from gene expression data with Bayesian networks?
Biochemical Society Focused Meeting on Systems Biology, Sheffield, July 2003
71. Husmeier D. (2003)
Application of Bayesian networks and MCMC in computational molecular biology
Invited talk to the Department of Computer Science, University of Sheffield, November 2003
72. Husmeier D. (2004)
Interpreting gene expression data
4 invited lectures given to postgraduate students in biology as part of their module on Postgenomic and
Pathway Biology at the Scottish Centre for Genome Technology and Informatics (GTI), January 2004.
73. Husmeier D. (2004)
Detecting interspecific recombination in DNA sequence alignments
Invited talk given to the Mathematical Genetics and Bioinformatics Seminar in the Department of
Statistics atOxford University, 4 May 2004
74. Husmeier D. (2004)
Detecting interspecific recombination in DNA sequence alignments
Invited talk, Adaptive & Neural Computation seminar, Institute for Neural & Adaptive Computation,
School of Informatics, Edinburgh University, 11 May 2004
75. Husmeier D. (2004)
Detecting mosaic structures in DNA sequence alignments
Invited talk to the Royal Statistical Society North Eastern local group, Newcastle, 25 February 2005
76. Husmeier D. (2005)
Multivariate analysis of gene expression data
4 invited lectures given to postgraduate students in biology as part of their module on Postgenomic and
Pathway Biology at the Scottish Centre for Genome Technology and Informatics (GTI), January 2005
77. Husmeier D. (2005)
Probabilistic modelling in bioinformatics
Invited lecture to M.Sc. students in the Division of Informatics, University of Edinburgh, on 2 March
2005
78. Husmeier D. (2005)
Learning local gene interaction networks from noisy gene expression data with probabilistic graphical
models
Invited tutorial given at the Workshop on Computational Methods in Systems Biology (CMSB05),
Edinburgh, 3 April 2005
79. Husmeier D. (2005)
Detecting Mosaic Structures in DNA Sequence Alignments
Invited keynote talk, Workshop on Statistics in Genomics and Proteomics
Monte Estoril, Portugal, 5-8 October 2005
80. Husmeier D. (2005)
Statistical methods for the detection of mosaic structures in DNA sequence alignments.
Invited talk, Department of Statistics, University of Glasgow, 9 November 2005.
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81. Husmeier D. (2005)
Two invited lectures on phylogenetics and systems biology, to be held in Canberra, November 2005,
organised by the Australian Mathematical Sciences Institute, Summer Symposium; see
http://www.maths.anu.edu.au/events/BioInfoSummer05/schedule.html .
APPENDIX A: COLLABORATIONS
BioSS collaborations

Wolfgang Lehrach
PhD project on in silico prediction of protein interactions

Dr Kuang Lin
SEERAD flexible fund systems biology project on host-pathogen interactions

Adriano Werhli
PhD project on machine learning methods in computational systems biology

Dr Frank Wright
Various collaborations in sequence and systems bioinformatics, including the TOPALi project
External collaborations

Dr Stuart Aitken, Dr Douglas Armstrong , Professor Andrew Millar, University of Edinburgh
Collaboration on Centre-of-Excellence SEERAD grant proposal

Dr Douglas Armstrong, Professor Chris Williams, University of Edinburgh
Co-supervision of PhD students

Dr Miles Armstrong, Dr Vivian Blok, SCRI
Collaboration on phylogenetic analysis of mitochondrial DNA sequences

Dr Paul Birch, SCRI
Collaboration on SEERAD-funded systems biology project

Professor Peter Ghazal, Thorsten Forster,
Scottish Centre for Genome Technology and Informatics (GTI)
Collaboration on the interferon pathway

Professor Wolfgang Urfer, Marco Grzegorczyk ,University of Dortmund (Germany)
Collaboration on reverse engineering gene regulatory networks

Anna Kedzierska, University of Wroclaw (Poland)
Collaboration on detecting recombination in DNA sequence alignments
APPENDIX B: FUNDING

PhD project on computational systems biology, CAPES (Brazil), GBP 72,000

Co-applicant for a flexible fund project on systems biology of host-pathogen interactions, SEERAD,
GBP 259,671

I am currently involved in tendering for a SEERAD funded Centre-of –Excellence (GBP 0.3 m). The first
proposal has been shortlisted to the second round of the selection process.
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APPENDIX C: CANDIDATE’S STATEMENT
Coming from a background in theoretical physics and machine learning, I moved into computational biology in 1999
with a postdoctoral research project on detecting evidence for interspecific recombination in DNA sequence alignments,
carried out at the SCRI unit of BioSS. Interspecific recombination is a process that results in the transfer of DNA/RNA
subsequences between different bacterial or viral strains. It constitutes an important source of genetic diversification in
fast-evolving pathogens, like HIV-1, and has been identified as a possible cause for the emergence of multiple drug
resistance in various bacterial genera, like Neisseria and Streptococcus. At the time of writing this text, the prospect of
avian flu virus H5N1 acquiring the ability to infect humans by horizontal gene transfer is a cause of general health
concern, and illustrates the increased interest in interspecific recombination as a powerful process of information
sharing in what is now referred to as the pathosphere (New Scientist, 8 October 2005, p.58).
In my recent work, I have developed various methods for detecting interspecific recombination. All these methods are
based on a phylogenetic approach and exploit the fact that interspecific recombination frequently results in a change of
the phylogenetic tree topology for the taxa involved in this process. In one approach [22], a sliding window is moved
along a given DNA sequence alignment. For every window position, the marginal posterior probability
over tree topologies is determined by means of a Markov chain Monte Carlo (MCMC) simulation. A probabilistic
divergence measure is then estimated, and significant peaks in the resulting signal indicate breakpoints between
different recombinant regions in the DNA sequence alignment. In a second approach [19,23], I have combined two
probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa,
and (2) a site graph (hidden Markov model) representing correlations between different sites in the DNA sequence
alignments. I have adopted a Bayesian approach to sample the parameters of the model from the posterior distribution
with MCMC [25,26], and the method has proven to allow the identification of recombinant breakpoints at a very high
spatial resolution. I have applied both methods to the detection of recombination in potato virus Y [34], and my
programs have been integrated into the user-friendly software package TOPALi [29]. I have recently extended my
modelling approaches to address the problem of differentiating between interspecific recombination and rate
heterogeneity [31], and I have supervised two Masters projects with the objective to explore further improvements in
the methodology.
I took up a Band 5 position at BioSS HQ in 2002. At that time, the molecular biology community in general, and SCRI
in particular, showed enthusiastic interest in microarray experiments. Microarrays were portrayed as a key new
technology that allowed biologists to monitor the expression of thousands of genes simultaneously and thus to obtain
snapshots of the active state of a complete genome under different experimental conditions. In my own work, I got
particularly intrigued by the prospect of reverse engineering gene regulatory networks from microarray expression data,
and I started to pursue the application of probabilistic machine learning methods based on Bayesian networks.
Bayesian networks are interpretable and flexible models for representing conditional dependence relations between
multiple interacting quantities, and they have been widely applied in machine learning and bioinformatics; see my
review in [45]. The rationale behind this approach is that the probabilistic nature of Bayesian networks is capable of
handling noise inherent in both the biological processes and the experimental procedures, which renders this approach
superior to deterministic inference methods. However, a major challenge for the reverse engineering of regulatory
networks from transcriptomic data is the fact that complex structures involving interactions between hundreds or
thousands of genes have to be learned from comparatively small data sets, typically containing only a few dozen time
points or experimental conditions. I have therefore addressed the important question of assessing the reliability of the
inference procedure. In a first study [28], I simulated gene expression data from a realistic molecular biological network
involving DNAs, mRNAs and proteins by numerically solving a system of coupled differential equations. I then tried to
infer regulatory networks from these data in a reverse engineering approach, using dynamic Bayesian networks and
Bayesian learning with MCMC. I am currently extending this evaluation with a PhD student to compare the
performance of different machine learning methods (Bayesian networks, relevance networks and graphical Gaussian
models) in the context of active learning from interventional data, based on preliminary explorations carried out via the
supervision of two MSc projects.
My future work will take a wider view of systems biology with the objective to develop and apply machine learning
methods to systematically integrate different types of postgenomic data. The rationale behind this approach is that
inference based on the fusion of different inherently noisy data will be more accurate than any method that is based on a
single data type alone, and that postgenomic data of heterogeneous nature highlight different aspects of the regulatory
pathways and are subject to complementary constraints that can be exploited in the inference process. I have pursued
initial work along this line with a PhD student [32,40,43], where we aim to learn protein interactions from amino acid
sequences with a probabilistic discriminative model. This work is motivated by the fact that high-throughput interaction
scans like yeast two-hybrid and phage display are expensive and intrinsically noisy. Hence, our objective is to more
specifically target or partially bypass them with a complementary in silico approach. I aim to extend this work into a
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BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
systems biology context along the line described above, whereby regulatory interaction networks are inferred by
systematically integrating sequence data, gene expression profiles, and putative protein interactions. This work is of
immediate relevance to ongoing projects on host-pathogen interactions at MRI and SCRI. Integrating my machinelearning based approaches with complementary computational and modelling expertise at SCRI and MRI will assist
molecular biologists in their endeavour to infer hypothetical regulatory pathways and pivotal regulatory genes within
different pathosystems. The ultimate objective of this work will be the identification of optimum candidate genes and
pathways to target to disrupt pathogenicity and enhance disease resistance in crop plants and livestock.
I expect to increasingly take on responsibilities for leading the research in bioinformatics and computational biology at
BioSS. I have supervised the projects of six Master students since 2003. I am currently the first supervisor of two PhD
students, and I am involved in the supervision of two further PhD students. I am also managing a postdoctoral research
assistant working on a SEERAD-funded flexible fund project, and I am involved in the collaboration on a proposal for a
SEERAD-funded Centre of Excellence, which will involve other scientists from BioSS, SCRI, Edinburgh University,
and Heriot-Watt University.
12.
CANDIDATE'S DECLARATION:
I confirm that I have read and am fully aware of the contents of Sections 1-11 of this proposal.
Candidate's signature ....................................................................................................
Date ......................................................
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BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
ANNEX C - CONTD
13.
HEAD OF DIVISION'S RECOMMENDATION:
(or comments in the case of a self-nomination)
I confirm that I have read this promotion proposal and considered it against the specified criteria for
promotion to the grade of ......., as set out in Annex B to Staff Code Section 9.2.
Signed ................................................................................................................(Head of Division)
Date .............................
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BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
ANNEX D
STAFF IN CONFIDENCE
BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL
SCIENCE MERIT PROMOTION PROCEDURE
PROMOTION PANEL ASSESSMENT FORM
PROMOTIONS w.e.f.
1 July 20.....
PANEL MEMBER (name)......................................................................................................................
CANDIDATE .............................................................................................................................................
PRESENT \PROPOSED GRADE ..........................................................................................................
INTERVIEW HELD ON ..............................
Attributes:
Assessment:
well fitted
Depth & breadth of scientific\
technical knowledge
Ability to innovate
Ability to plan
Ability to collaborate
Professional reputation
Achievements
Productivity
Leadership abilities
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
not fitted
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
OVERALL ASSESSMENT:
well fitted
fitted
marginally
above
marginally
below
not fitted
1
2
3
4
5
(circle, as appropriate)
NB: The attribute criteria are given in BBSRC SC Part II, Section 9.2, Annex B)
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AMDT 182 – APR 2002
BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
ANNEX E
STAFF-IN CONFIDENCE
BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL
SCIENCE MERIT PROMOTION PROCEDURE
PROMOTION INTERVIEW PANEL REPORT FORM
PROMOTIONS w.e.f.
July 20 .....
CANDIDATE .............................................................................................................................
PRESENT \PROPOSED GRADE ..........................................................................................
INTERVIEW HELD ON .........................................................................................................
PANEL MEMBERS
............................................................................... (Chair)
...............................................................................
...............................................................................
...............................................................................
PANEL RECOMMENDATION:
well fitted
fitted
marginally
above
marginally
below
not fitted
1
2
3
4
5
(circle, as appropriate)
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AMDT 182 – APR 2002
BBSRC STAFF CODE – SECTION 9.2 - ANNEXES
ANNEX E - CONTD
PANEL REPORT
a. (to be conveyed to candidate):
b. Supplementary comments (confidential to Board and Panel members):
DIRECTOR'S DECISION
PROMOTION APPROVED \ REJECTED (delete whichever is inappropriate)
SIGNED (DIRECTOR) ...........................................................................................................
DATE ......................................
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AMDT 182 – APR 2002