1. Serono Science Scientific computing and high performance applications

2. Research Computing in Serono
Hardware environment
High performance computing applications

3. Drug development pipeline
Target discovery
Target validation
Screening + H2L
PCD
Phase I/II
Phase III/IV Marketing
Proteomics
Genomics
Chemistry
Human Genetics
Biostatistics
Transcriptomics
Mouse genetics
Cell biology
Pharmacology
Sciences
WGS
uArrays
Protein arrays
siRNA
caliper
combichem MS
Taqman
imaging
cellomics
x-ray
RMN
Y2H
HTS
Technologies
genomes
Transcript map
SNPs
Protein structure
Patient data
protein map
interactions
screening data
phenotype
images
haplotype
Data types
pathways
Structure/activity

4. Research Computing Vision & Missions
Use in-silico technologies to help identify and progress therapeutic proteins and small molecules that will successfully feed the development pipeline
By:
Research Knowledge Management Delivering across Research an integrated information environment that puts scientific data, information and knowledge at the scientist’s fingertips
Computational Life Science Developing cutting-edge scientific applications enabling in-silico drug discovery and driving Serono’s competitive advantage
Advanced Data Analysis Providing advanced and pervasive data analysis competencies to make sense of high-throughput and complex data.
Research IT environment Providing the computing and communication infrastructure to deliver the vision

5. Research computing activities
1. Data processing
Technology driven
2. Predictions and simulation
3. Data analysis
Interpretation 1s
-1s m
4. Data management

6. Advanced Data Analysis - Issues
Data complexity
Amount of data
Analysis cannot be performed in silos – we need information systems able to correlate data available from all sorts of experimental information (Genome scans, DGE, RNAi, Cell assays, proteomics, interactions, phenotypes)
2000
2002
2004
2001-3
High content cell assays, genomic sequence, QSAR
High density microarrays as a discovery platform
Genome scan data – 100’000 SNP’s, hundreds of patients, several diseases
Biomarkers identification through proteomics and trasncriptomics
Compendium, Virtual Combinatorial Library
Multidimensional decision making
Microarrays: complex data (time series, complex tissues deconvolution, disease models, full transcriptome)
2004-7

7. Grid for the life sciences – differences
Physics
Biology
Theory
« complete »
Inexistent or imprecise
Level of abstraction (model)
Single
Multiple
Volume
Very high
Low-medium
Data complexity
Low

8. User-friendly interfaces (Web based)
Generic End-user Access
in silico generation tools e.g. Text mining, Data Analysis
Corporate Database Core
Integrated
Oracle-Based
Systems
Drill-down
E-notebook
Publish
LIMS QC
LIMS QC
LIMS QC
Specialized, complex power-user interfaces

9. HPC Hardware environment
SGI Origin proc (cc-numa), IRIX, 128 GB
SGI Altix 3700 BX2 16 proc
Timelogic Decypher FPGA bioinformatics accelerator x 4
SGI Origin proc
Linux Xeon cluster, 50 proc
10 TB CXFS SAN (Geneva only)
Computational chemistry (docking, combichem, compendium, pharmacophore, structure resolution) Bioinformatics (public domain tools, sequence databases, peptide identification, in-silico modeling)
Blast, SW, profiles
Same as above, Boston, Paris Distributed data storage

10. High performance computing applications in Serono today
Large scale sequence to sequence comparisons
Genome wide analysis (microRNA, focused gene prediction, gw profiles, etc.)
Sequence data base monitoring
Gene index and data mapping
Large scale proteomics (peptide identification)
Virtual screening
In-silico biology

11. Smart is better than More
In combinatorial chemistry design, one scaffold and 4 groups of 800 reagents each generate a library of 320 billions virtual compounds
Virtual Combinatorial Database
Enumerated substracture search would take years of CPU time and 1 petabyte of storage
A proprietary non-enumerated search retrieves hits in just a few seconds
Fast pre-filtering of compounds reduces amount of compounds for time-consuming docking studies
Useful for new compound acquisition, known protein target structure, not for primary screen (replating)
Usual size of virtual screens in Serono: ~1000 compounds
Virtual Screening

12. Future grid applications
Large scale in-silico modeling
Protein-protein interaction
QM-based, dynamic virtual screening
Data grids
Imaging

13. Past grid evaluations (corporate PC idle cycles)
High deployment costs – IT resources
Concern about availability of PC resources – habits and procedures
Foreseen replacement of desktop by even less available laptops
Modification of software to run effectively on the grid
Previous studies show that a large corporate grid of 1000 desktops is not more efficient than a 64 proc dedicated cluster (Novartis)
The in-house idle-cycle grid model is not efficient

14. Issues in the pharma industry
IP considerations
Competitive intelligence
Security policies
Obsession with proprietary data and know-how
Is the current model of « all in-house » sustainable?
Distributed (grid-enabled) public domain bioinformatics services will anyway become pervasive and will superceed capabilities available in-house