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Bioinformatics Research Centre
University of Glasgow
David Gilbert
www.brc.dcs.gla.ac.uk
Department of Computing Science,
University of Glasgow
David Gilbert:
[email protected]
BRC Glasgow
1
Bioinformatics
•Bio - Molecular Biology
•Informatics - Computer Science
•Bioinformatics - the study of the application of
- molecular biology, computer science, artificial
intelligence, statistics and mathematics
- to model, organise, understand and discover
interesting information associated with the large scale
molecular biology databases,
- to guide assays for biological experiments.
David Gilbert:
[email protected]
(Computational
Biology - USA).
BRC Glasgow
2
Bioinformatics in context a new discipline?
Computing
Maths &
Stats
?Psychology?
Physical
Sciences
Life
sciences
David Gilbert:
[email protected]
BRC Glasgow
3
Bioinformatics in context
(applications)
David Gilbert:
[email protected]
BRC Glasgow
4
How can we analyse the flood of data ?
Data: don't just store it, analyze it ! By comparing
sequences, one can find out about things like
• How organisms are related
& evolution
• How proteins function
• Population variability
• How diseases occur
David Gilbert:
[email protected]
BRC Glasgow
5
Separating sheep from goats...
David Gilbert:
[email protected]
BRC Glasgow
6
Dirty data?
Big Horn Sheep [Ovis canadensis]
The Big Horn Sheep [Ovis canadensis] is a large North American species with a brown
coat, which turns to bluish-grey in winter.
It is so named from the size of the horns of the ram, which often measure over 1 m/3.3 ft
round the curve.
Classification:
David Gilbert: Ovis canadensis is in family Bovidae, order Artiodactyla
[email protected]
BRC Glasgow
7
Data, information, knowledge …
• data : nucleotide sequence
• information : where are the “genes”.
control
statement
Termination
(stop)
TATA box
control
statement
start
gene
Found using classifier, pattern, rule which has been mined/discovered
• knowledge : facts and rules
If a gene X has a weak psi-blast assignment to a function F
–and that gene is in an expression cluster
–and sufficient members of that cluster are known to have function F,
 then believe assignment of F to X.
David Gilbert:
[email protected]
BRC Glasgow
8
Some projects at the
Bioinformatics Research Centre
David Gilbert:
[email protected]
BRC Glasgow
9
David Gilbert:
[email protected]
BRC Glasgow
10
Rat-Mouse-Human
David Gilbert:
[email protected]
BRC Glasgow
11
Indexing
Ela Hunt [email protected]
• String indexing structures can be used to index
DNA, proteins, XML and phylogenetic trees
• All data is read once, index in created on disk
• Index reduces the search space of the query (we
read a % of disk only)
David Gilbert:
[email protected]
BRC Glasgow
12
Distributed databases and computation
Cardiovascular Functional Genomics
• -£5.4 million project, 5 UK Universities: Glasgow, Leicester, Edinburgh, Oxford,
Imperial; + Maastricht
• Led by Clinicians
• Combined studies:
– scientific models of disease (Rat)
– parallel studies of patients
– large family and population DNA collections
• 3 pronged approach
– Targeted transcript sequencing
– Microarray gene expression profiling
– Comparative genome analysis.
• Data generated at each of the 5 sites & made available for analysis:
• Issues of distributed data and computation.
• Mapping gene sequences Rat  Mouse Human
– an added layer of complexity in the computation.
David Gilbert:
[email protected]
BRC Glasgow
13
Wellcome Trust: Cardiovascular
Functional Genomics
Glasgow
Shared data
Edinburgh
Public curated
data
Leicester
Oxford
London
David Gilbert:
[email protected]
BRC Glasgow
Netherlands
14
BRIDGES: BioMedical Research Informatics
Delivered by Grid Enabled Services
•
National e-Science Centre, Bioinformatics Research Centre, IBM UK Life Sciences
•
Incrementally develop and explore database integration over 6 geographically distributed research
sites within the framework of the large Wellcome Trust biomedical research project
Cardiovascular Functional Genomics.
•
Three classes of integration will be developed to support a sophisticated bioinformatics
infrastructure supporting:
– data sources (both public and project generated),
– bioinformatics analysis and visualisation tools,
–
research activities combining shared and private data.
•
The inclusion of patient records and animal experiment data means that privacy and access control
are particular concerns.
•
An exploration of index factories accelerating sequence processing will test the hypothesis that the
Grid makes a new class of e-Science indexes feasible. Both OGSA-DAI and IBM DiscoveryLink
technology will be employed and a report will identify how each performed in this context.
David Gilbert:
[email protected]
BRC Glasgow
15
Functional Genomics
~44,000
GENES
David Gilbert:
[email protected]
~33% OF GENES HAVE
UNKNOWN FUNCTION
BRC Glasgow
16
Ali Al-Shahib
Chao He, Mark Girolami
Solution……
• Solve the problem of the twilight zone (sequence
alignments below 30% sequence identity)
• How?
• Predict protein function using an alternative method to
BLAST:
• Predict protein functional class from sequence, structural
and phylogenetic features using machine learning
• Combination of these (computationally and statistically)
would provide the biologists like yourselves with the most
accurate functional prediction of proteins that fall in the
twilight zone.
David Gilbert:
[email protected]
BRC Glasgow
17
Molecular Evolution:
A Phylogenetic Approach
Rod Page
[email protected]
Human
gene duplication
Locating genome
duplications
Q: did one or more
genome-wide events
affect all gene families?
Human
Mouse
Reptiles + Birds
Human
Lungfish
Mouse
Lamprey
David Gilbert:
[email protected]
Mouse
happened
somewhere
here
BRC Glasgow
Teleosts
Sharks & Rays
Lamprey
18
TOPS
Protein
topology
David Gilbert,
Juris Viksna,
Gilleain Torrance (BRC,
Glasgow),
David Westhead and
Ioannis Michalopoulos
(Leeds)
BBSRC/EPSRC funded
David Gilbert:
[email protected]
BRC Glasgow
19
Pattern search:
TIM Barrel
David Gilbert:
[email protected]
BRC Glasgow
20
Structure comparison
2bop (probe)
against
(subset of) CATH
David Gilbert:
[email protected]
BRC Glasgow
21
TOPS comparison server: www.tops.leeds.ac.uk
PDB file
TOPS diagram (graph)
(v.fast)
(slower)
Pairwise
comparison to
structures in
database
Matches to motif
library
David Gilbert:
[email protected]
BRC Glasgow
22
Protein design
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Brian Kuhlman,1 Gautam Dantas,1 Gregory C. Ireton,4 Gabriele Varani,1,2 Barry L. Stoddard,4 David Baker1,3
“A major challenge of computational protein design is the creation of novel proteins with
arbitrarily chosen three-dimensional structures.
Here, we used a general computational strategy that iterates between sequence design and
structure prediction to design a 93-residue /ß protein called Top7 with a novel sequence
and topology.
Top7 was found experimentally to be folded and extremely stable, and the x-ray crystal
structure of Top7 is similar (root mean square deviation equals 1.2 angstroms) to the design
model.
The ability to design a new protein fold makes possible the exploration of the large regions
of the protein universe not yet observed in nature.”
1 Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
2 Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
3 Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
4 Division of Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
David Gilbert:
[email protected]
Science. 2003 Nov 21;302(5649):1364-8
BRC Glasgow
23
Protein design
Generation of starting models.
“The target structure for the de novo design process
can range from a detailed backbone model to
a back-of-the-envelope sketch.”
“Because we aimed to create a novel protein fold,
we selected a topology not present in the PDB
according to the
Topology of Protein Structure (TOPS) server (17).”
David Gilbert:
[email protected]
BRC Glasgow
24
Use of TOPS for protein design
User = [email protected]
Submitted at 20:29:51 on 3/06/03
Structure code = top7a
type = PDB (user declared), Database = atlas
Details of sheets etc (including all connected SSEs): Sheet: [6,7,4,1,2]
======================================================
Domain
Code
Rank
Comparison time : 43 sec
top7a target_query
0
1bbi00 4.10.100.10.1
7
1pi200 4.10.100.10.1
7
1sro00 2.40.29.10.1
7
1atx00 2.20.20.10.1
9
2sh100 2.20.20.10.1
9
1vcc00 3.30.66.10.1
11
1hpm02 3.10.140.10.1 12
1csp00 2.40.50.40.1
13
2snv01 2.40.10.20.3
13
3tss02 2.40.50.50.3
13
1bcpF0 2.40.50.50.2
14
1bovA0 2.40.50.30.2
14
1tle00 2.10.25.10.1
14
1cdb00 2.60.40.10.1
15
1ckmA3 4.10.87.10.1
15
1kxf01 2.40.10.20.3
15
1svpA1 2.40.10.20.3
15
2pkaX0 2.40.10.20.1
15
1apo00 2.10.25.10.6
16
NEEheEC 1:2A
1ate00 2.10.40.10.1
16
1aww00 2.30.30.10.1
16
1cuk01 2.40.50.80.1
16
David Gilbert:
[email protected]
Top7a
1:4A 2:4R 4:6R 4:7A 6:7A 1:4R 4:6R
BRC Glasgow
25
Use of TOPS for protein design
David Gilbert:
[email protected]
BRC Glasgow
26
Systems biology – some definitions
• Systems biology is the study of all the elements in a
biological system (all genes, mRNAs, proteins, etc)
and their relationships one to another in response to
perturbations.
• Systems approaches attempt to study the behaviour of
all of the elements in a system and relate these
behaviours to the systems or emergent properties
David Gilbert:
[email protected]
BRC Glasgow
27
A Framework for Systems Biology
(Ideker, Galitski & Hood, 2001)
• Define all of the components of the system
• Systematically perturb and monitor components of the system
• Reconcile the experimentally observed responses with those
predicted by the model
• Design and perform new perturbation experiments to
distinguish between multiple or competing model hypotheses
David Gilbert:
[email protected]
BRC Glasgow
28
New database technologies for storing the output from highthroughput biological experiments
Andrew Jones
•
•
•
•
•
Proteomics – study the set of proteins expressed in
a sample
Complex, variable output:
• High-Resolution images
• Numerical data generated by lab. equipment
and software
• Human Annotation
The data is not suitable for storage in a standard
relational database
Storage, retrieval and exchange of data is important
XML (Extensible Markup Language) is being
investigated for storing such data
David Gilbert:
[email protected]
BRC Glasgow
29
• Maintained by National
Library of Medicine
• Free of charge, since
1997
• > 10 million references
since 1971
• > 4000 biomedical
journals
• > 80% in English
• > 80% have an abstract
"Biochemical Network Data Mined
from Scientific Texts"
Te Ren (PhD student)
with CXR Biosciences.
David Gilbert:
[email protected]
BRC Glasgow
30
Data complexity
Methionine Biosynthesis in E.coli
L-aspartate
aspartate biosynth.
aspartate kinase II/homoserine dehydrogenase II
2.7.2.4
catalyzes
expression
codes for
ATP
ADP
metBL operon
aspartate semialdehyde deshydrogenase
asd
metL
expression
codes for
catalyzes
1.2.1.11
L-Aspartate-4-P
NADPH; H+
NADP+; Pi
L-Aspartate semialdehyde
lysine biosynth.
metB
catalyzes
metA
represses
1.1.1.3
homoserine-O-succinyltransferase
expression
codes for
catalyzes
NADPH;H+
NADP+
L-Homoserine
threonine biosynth.
Succinyl SCoA
2.3.1.46
HSCoA
represses
represses
Holorepressor
cystathionine-gamma-synthase
expression
codes for
catalyzes
4.2.99.9
aplha-succinyl-L-Homoserine
L-Cysteine
Succinate
is part of
represses
cystathionine-beta-lyase
metC
expression
codes for
Aporepressor
4.4.1.8
catalyzes
Cystathionine
H2O
Pyruvate; NH4+
represses
Homocysteine
metE
Cobalamin-independent homocysteine transmethylase
expression
codes for
expression
codes for
represses
expression
codes for
metH
expression
codes for
metR
2.1.1.13
2.1.1.14
up-regulates
metJ
5-Methyl THF
catalyzes
THF
catalyzes
Cobalamin-dependent homocysteine transmethylase
inhibits
L-Methionine
metR activator
2.5.1.6
ATP
Pi; PPi
is part of
inhibits
L-Adenosyl-L-Methionine
Biochemical networks
DNA chip
experiment
Transcription profiles
• Pathway navigation
• Pathway comparison
• Pathway motif discovery
• Pathway simulation
Visualization
Clustering
Clusters of
co-regulated genes
Functional meaning ?
Pathway extraction
in metabolic reaction graph
Putative
metabolic pathways
Matching against
metabolic pathway
database
Known
pathways
• High-level abstraction inferred from low-level descriptions
• Novel pathways from gene expression experiments
Novel
pathways
L-Aspartate
2.7.2.4
L-aspartyl-4-P
A Software System for
Pattern Matching and Motif Discovery
in Biochemical Networks
Sebastian Oehm
[email protected]
1.2.1.11
L-aspartic semialdehyde
L-Aspartate
2.7.2.4
L-aspartyl-4-P
1.2.1.11
L-aspartic semialdehyde
1.1.1.3
1.1.1.3
L-Homoserine
L-Homoserine
2.3.1.31
O-acetyl-homoserine
4.2.99.10
Homocysteine
2.1.1.14
L-Methionine
• Design a suitable data model using bipartite graphs
• Define patterns and develop algorithms for pattern
matching in biochemical networks
• Define pathway motifs and develop algorithms for
motif searching in biochemical networks
• Develop algorithms for automated motif discovery
• Develop algorithms to search for the largest common
part of two or more biochemical networks
• Develop a measure of similarity for pathway
comparison
2.3.1.46
Alpha-succinyl-LHomoserine
4.2.99.9
Cystathionine
4.4.1.8
Homocysteine
2.1.1.14
L-Methionine
2.5.1.6
2.5.1.6
S-Adenosyl-L-Methionine
S-Adenosyl-L-Methionine
David Gilbert:
S.cerevisiae
[email protected]
BRC Glasgow
E.coli
33
Biochemical Pathway Simulator
A Software Tool for Simulation &
Analysis of Biochemical Networks
DTI ‘Beacon’ project, £0.9M, 4 years
Muffy Calder
David Gilbert
Walter Kolch
Keith van Rijsbergen
Brian Ross
Oliver Sturm
David Gilbert:
[email protected]
BRC Glasgow
34
Not a toy problem!
Experimental Data
David Gilbert:
[email protected]
Analysis
BRC Glasgow
35
Complexity: real
bioinformatics
Closing the loop from
wet lab to in-silico
Mitogens
Growth factors
Abstract
model
Receptor
receptor
e
Ras
n
ki
P P
Raf
as
P
P P
MEK
P P
ERK
cytoplasmic substrates
Elk
SAP
Gene
Human feedback (in-the-loop)
Simulator
DATA
Analysis
Pathway
Editor
Literature
Apoptosis
Rules
Database
Apoptosis
Text miner
Simulator
Concurrency theory
Bio
Lab/Literature
David Gilbert:
[email protected]
Bioinformatics
Tools, database, interface
BRC Glasgow
36
Web portal
Lab
MAPK
User Interface
Database
MAPK
Proliferation (Cell division) vs Differentiation (Neurite out
in PC12 cell model
NGF (50 ng/ml)
Differentiation into
nerve cell type
EGF (50 ng/ml)
 Proliferation
cell division stimulated without
neurite outgrowth
David Gilbert:
[email protected]
BRC Glasgow
neurite outgrowth
37
Dynamic Behaviour of the Network
Receptor
Receptor
Ras
Receptor
cAMP
Ras
cAMP
PKA
Ras
PKA
Raf-1
Raf-1
B-Raf
Raf-1
MEK1,2
MEK1,2
MEK1,2
ERK1,2
ERK1,2
ERK1,2
Cell growth
Raf-1 is expressed in all
cells, and its activation
induces ERK activation
David Gilbert:
[email protected]
Growth arrest
Many receptors that activate ERK
also elevate cAMP levels leading
to activation of PKA. PKA inhibits
Raf-1 and blocks ERK activation
BRC Glasgow
Cell growth
However, cAMP induces activation
of B-raf. In cells which express
B-raf, cAMP activates the ERK
pathway despite of Raf-1 inhibition.
38
David Gilbert:
[email protected]
BRC Glasgow
39
Mobility
Sometimes a signal sent in a communications network can change the connections or topology of that network. In the example below, a cell-phone is
being carried out of range of Cell 1. The base station must send the frequency of the appropriate new Cell (Cell 2) to the phone. The phone connects to
Cell 2 and discards its previous link to Cell 1.
Frequency
Cell 2
Conversation
Conversation
Cell 1
Cell 2
Frequency
Cell 2
Conversation
Conversation
Base
Base
David Gilbert:
[email protected]
BRC Glasgow
40
In biochemical networks, a protein can be granted or denied the opportunity to interact with certain other
molecules by exchange factors, effectively changing the network topology dynamically. In the example below,
the protein Ras is bound to a molecule of GDP, which renders Ras inactive. A molecule of SoS can interact with
this Ras-GDP complex, causing the GDP to be exchanged for GTP. The Ras-GTP complex is active, permitting
interaction with the protein Raf.
Ras
Ras
Raf
GDP
GTP
SoS
GDP
GTP
David Gilbert:
[email protected]
BRC Glasgow
41
Reusable Subcomponents of a Solution for
Offline Integration of 3rd party Databases
Integrator
Extracted
Lit. Data
Schema
Translator
Record
Matcher
Integrated
Database
Record
Merger
aMaze DB
MAPK
source data
cAMP PK
source data
Input
Schemas
David Gilbert:
[email protected]
Trans Local
Schemas
•
Record
Matching
Rules
Default
Values
Cross-ref
Index
Conflict
Resolution
Rules
Target Schema
By-products of the total process may correspond to other
reusable sub-services
– Schema Translation – various schema definition langs are
translated into one common, interpretable schema lang.
– Record Matching – builds a cross reference index that
identifies records about a “same entity” and records the
source and location of the matching records. Two or more
records may match.
BRC Glasgow
42
Validation
Current Bottlenecks in Drug Development
Drug target discovery: What is a good drug target? How do we select it?
Drug target validation: Does hitting the target change the biological response?
Side effects: What else is affected when the selected target is hit?
Lead Compound Selection: Which compounds should be taken further for
development. What properties should the drug have?
David Gilbert:
[email protected]
BRC Glasgow
43
Validation
Current Bottlenecks in Drug Development
Drug target discovery: What is a good drug target? How do we select it?
Drug target validation: Does hitting the target change the biological response?
Side effects: What else is affected when the selected target is hit?
Lead Compound Selection: Which compounds should be taken further for
development. What properties should the drug have?
David Gilbert:
[email protected]
BRC Glasgow
44
Validation
Current Bottlenecks in Drug Development
A robust Pathway Simulation Software can help to …
Drug target discovery: What is a good drug target? How do we select it?
Select targets by defining its topology & function in the regulatory networks.
Drug target validation: Does hitting the target change the biological response?
Validate the target by predicting how the biological response should change.
Side effects: What else is affected when the selected target is hit?
Predict side effects to allow early and targeted testing.
Lead Compound Selection: Which compounds should be taken further for
development. What properties should the drug have?
Predict the optimal drug profile to improve selection criteria.
David Gilbert:
[email protected]
BRC Glasgow
45
Validation
What we propose …
PC12 cell model of neuronal differentiation
EGF
Ras
Transient
ERK
activity
Raf-1
MEK
NGF
Rap
proliferation
ERK
B-raf
Sustained
ERK activity
differentiation
Target Validation: Predict & test the effect of Raf-1 and B-Raf inhibitors to the
biological response to EGF vs. NGF.
Lead Compound Selection: Predict & test which inhibitory efficacy is
necessary and sufficient to achieve the desired biological response.
David Gilbert:
[email protected]
BRC Glasgow
46
Bionanotechnology &
Bioinformatics
Nanofab &
cell culture
Fab methodology
Physical substrate
Measured
cell
behaviour
Dynamic behaviour
Model of cell
behaviour
Biochemical
environment
(other cells +
biochemicals)
Morphology
Adhesion
Cell shape
Gene expression
Bioinformatics
Genetic
engineering
External
databases
David Gilbert:
[email protected]
Proteome
Other pathway data
BRC Glasgow
47
Machine Learning for Bioinformatics
• Classification
• Clustering
• Characterisation
• Techniques:
–
–
–
–
–
–
ensemble methods
decision trees
inductive logic programming
pattern discovery
Statistical approaches
SVMs
David Gilbert:
[email protected]
BRC Glasgow
48
Cancer Classification Problem
(Golub et al 1999)
ALL
acute lymphoblastic leukemia
(lymphoid precursors)
David Gilbert:
[email protected]
BRC Glasgow
AML
acute myeloid leukemia
(myeloid precursor)
49
Machine Learning Approach
Machine
Learning
Gene
Expression
Profiles
David Gilbert:
[email protected]
C4.5
SVM
k-NN
ANN
BRC Glasgow
Classifier
ALL AML ALL AML
50
Biological Data: Distributed and Heterogeneous!!
Protein
Sequence
LPSYVDWRSA
ECGGCWAFSA
TSGSLISLSE
NTRGCDGGYI
GGINTEENYP
Structure
Function
GAVVDIKSQG
IATVEGINKI
QELIDCGRTQ
TDGFQFIIND
YTAQDGDCDV
Microarray analysis
Gene expression
David Gilbert:
[email protected]
Morphology
BRC Glasgow
51
Integrative Machine Learning
Aik Choon Tan
(Pratt
Emotif)
David Gilbert:
[email protected]
BRC Glasgow
52
What kind of computational approaches do we use?
• Operations over
– sequences (match)
– trees (e.g. suffix trees, supertree, joining, ...)
– graphs (sub-graph isomorphism, maximal common subgraph, path
searching)
• Data modelling, databases, data conversion
• Machine learning, knowledge discovery, pattern discovery,...
• Clustering
• Theorem proving, concurrency analysis,…
• Integration: data, knowledge
• Data visualisation
• Web services, Grid, Coarse Grain parallelism, eScience,...
David Gilbert:
[email protected]
BRC Glasgow
53
Latest from BRC
• New Systems Biology lab (March 9)
• Web services, www.brc.dcs.gla.ac.uk
• Research teams:
Databases & Visualisation
Grid & eScience
Functional genomics
Machine learning
Structural bioinformatics
Systems biology
(Ela Hunt)
(Richard Sinnott)
(David Leader)
(Mark Girolami)
(Pawel Herzyk)
(David Gilbert)
• Teaching: MScIT Bioinformatics Strand
David Gilbert:
[email protected]
BRC Glasgow
54
BRC members
•
•
•
•
•
Investigators:
– Yves Deville
(Biochemical Networks)
dcs
– David Gilbert
(Systems biololgy, Protein structure)
dcs
– Mark Girolomi
(Machine learning)
dcs
– Pawel Herzyk
(Protein structure)
ibls
– Ela Hunt (Database indexing, Data integration, Visualisation,…)
dcs
– David Leader
(Visualisation tools)
ibls
– Gerhard May
(Signalling pathways)
ibls
– Rod Page
(Phylogenetic trees)
ibls
– Richard Sinnott
(Grid computing / eScience)
dcs
– Juris Viksna
(Graph algorithms)
dcs
Research Assistants: Micha Bayer, Rainer Breitling, Neil Hanlon, Derek Houghton, Richard
Orton, Evangelos Pafilis, Oliver Sturm, Gilleain Torrance
Research students: Ali Al-Shahib, David Cook, Iain Darroch, Amelie Gormand, Susan Fairley,
Robert Japp, Andrew Jones, Julie Morrison, Te Ren, Aik Choon Tan, Tim Troup, Mallika
Veeramalai
Executive Assistant: Margaret Jackson
Associated: Malcolm Atkinson, Ernst Wit, John McClure, Mathis Riehle, Des Higham, Oliver
Sand
David Gilbert:
[email protected]
BRC Glasgow
55
Funding sources
EPSRC
BBSRC
MRC
Wellcome Trust
DTI
Scottish Enterprise
Synergy
Carnegie Trust
Royal Society
Daiwa Foundation
SHEFCE
EU
David Gilbert:
[email protected]
BRC Glasgow
56
Scottish Bioinformatics Forum
• Network of Bioinformatics researchers and industries in Scotland
• A vehicle for developing Scotland as a Centre of Bioinformatics
Excellence
• Nodes in Glasgow, Edinburgh, Dundee, Aberdeen, ...
• Promoting collaborative research
• Development of a Bioinformatics educational programme
• www.sbforum.org, [email protected]
Visionary Meeting, 27 May (Zoology Building)
Keynote : Prof Thornton
Director of the European Bioinformatics Centre
www.brc.dcs.gla.ac.uk/events.html
David Gilbert:
[email protected]
BRC Glasgow
57
David Gilbert:
[email protected]
BRC Glasgow
58
Sun GridEngine
Bioinformatics Research Centre
Davidson Building: 15 workstations + visitors’ facilities
File Database Unix App
server server
server
firewall
Web
server
1TB
Microsoft
App server
Cluster
Scotgrid+
2x100 CPU
5 TB
3TB
17 Lilybank Gardens
Kelvin
Building
Boyd-Orr Building
(backup)
David Gilbert:
[email protected]
BRC Glasgow
59
www.brc.dcs.gla.ac.uk
David Gilbert:
[email protected]
BRC Glasgow
60
Where we are
Vet School
Beatson
Institute
Department of Computing Science
BRC
Functional
& Functional
Genomics;
Genomics
Centre(Joseph
for CellBlack)
Engineering
NeSC Hub
Medicine &
Theraputics
David Gilbert:
[email protected]
BRC (in Davidson Building)
BRC Glasgow
61
BRC location
David Gilbert:
[email protected]
BRC Glasgow
62
Bioinformatics Research centre (230m2)
Gardiner lab (wet lab)
Visitors’area
Visitors’area
David Gilbert:
[email protected]
BRC Glasgow
63
The Future
Closing the loop from wet lab to in silico !
Collaboration!
www.brc.dcs.gla.ac.uk
David Gilbert:
[email protected]
BRC Glasgow
64