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The Systems Biology Software Infrastructure

Systems Biology Software Infrastructure overview

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Overview presentation of the Systems Biology Software Infrastructure

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Page 1: Systems Biology Software Infrastructure overview

The Systems Biology Software Infrastructure

Page 2: Systems Biology Software Infrastructure overview

‘A new infrastructure to streamline the

connection between data, models, and

analysis,

allowing the updating of large scale

data, models

and analytic tools with greatly reduced

overhead’

SBSI objective

Page 3: Systems Biology Software Infrastructure overview

Graphical

Notation

Network Inference

Process Algebras

Model analysis

Existing knowledge

High-resolution data

High-throughput

data

New knowledge

Static models

Kinetic models

Systems Biology Software Infrastructure™

Kinetic Parameter Facility

RNA metabolism

Her2/ERK signalling

Systems Biology Research, CSBE view

Circadian clock

Page 4: Systems Biology Software Infrastructure overview

Current people involved in SBSICore developers

EPCC

Test Models and Evaluation

Project management

Circadian clock modellers

Stephen Gilmore PI

Nikos Tsorman Neil Hanlon

Galina Lebedeva

Alexey Goltsov

Azusa Yamaguchi

Kevin Stratford

People previously involved with SBSI

Shakir AliAnatoly Sorokin

Treenut SaithongStuart Moodie

Igor Goryanin

Ozgur Akman

Carl Troein

Biopepa integration

Adam Duguid

Richard Adams

Requirements & Numerics

Andrew Millar

Page 5: Systems Biology Software Infrastructure overview

Parallelized global parameter optimization – for everyone!

Develop client application

Integrate at least 1 external software package

SBSI goals 2008-2009

Page 6: Systems Biology Software Infrastructure overview

Parameter Estimation Problem

• Building predictive models –challenging problem in Systems Biology

• Parameter estimation – critical stage in model development

• Multiple data sets for model calibration

• Global optimization needed due to complex cost landscapes • Genetic /evolutionary techniques perform well.

• Circadian clock modellers have existing high-quality time-series data to fit.

Page 7: Systems Biology Software Infrastructure overview

Global parameter optimisation is compute intensive !

MacBook Pro2.2 GHz, intel Core 2 Duo

IBM BlueGene L32 node, 64 proc

Total optimization time for 100 generations

> 25 h Approx. 20 min;(BG is >75 times faster!)

Weimann mammalian circadian core oscillator 7 ODEs, 24 parametersUsing synthetic dataParallelized genetic algorithm

Page 8: Systems Biology Software Infrastructure overview

Performance scales well with increasing processor cores

Page 9: Systems Biology Software Infrastructure overview

Testing, testing, testing….

Rastrigin

‘abc_1’

VderPol

Goldbeter clock

Biomodels clock models

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Multi-objective optimisation

Page 12: Systems Biology Software Infrastructure overview

Optimizing Circadian Clock models with experimental data

Locke 2 loop model from Biomodels (57 params, 13 species)

Using BG/L 128 nodes, it finished at 63140th generation by non-improvement criteria.Run-time 46 hours. 0-6740 :FFT +Chi-squared674o – end : Chi-squared

Page 13: Systems Biology Software Infrastructure overview

Integration of other CSBEprojects

BioPepa ✔ EPE

Outline of SBSI design

✔Command line

SBSI Web Interface

SBSI Dispatcher

(Task Manager)✔ Compile C codes✔Submit jobs to HPC

✔Retrieve results✔Provide job status

SBSI Numerics

Numerical algorithms and Frameworks for

- Global optimisation ✔-Sensitivity analysis

- Bifurcation analysis

core

SBSI Visual

✔ Desktop application✔ Upload and edit SBML models✔ Run simulations✔ Interact with external repositories✔ Visualisation of data and results

SBSI clients

Eddie (ECDF)

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Integration of other CSBEprojects

BioPepa ✔ EPE

Outline of SBSI design

SBSI repository

Models (SBML)

Data ( SBSI standard format):-experimental data-simulation results

Command line

SBSI Web Interface

SBSI Dispatcher

(Task Manager)✔ Compile C codes✔Submit jobs to HPC

✔Retrieve results✔Provide job status

SBSI Numerics

Numerical algorithms and Frameworks for

- Global optimsation ✔-Sensitivity analysis

- Bifurcation analysis

core

SBSI Visual

✔ Desktop application✔ Upload and edit SBML models✔ Run local and remote simulations✔ Interact with external repositories✔ Visualisation of data and results

SBSI clients

Eddie (ECDF)

Plasmo, Robust

Page 18: Systems Biology Software Infrastructure overview

Aims early 2010

Move all code to SourceForge, encourage open-source access

Publish SBSI paper

Integrate Edinburgh Pathway Editor

Develop plugin mechanism for SBSI Dispatcher to connect to other HPCs, Grid?

Page 20: Systems Biology Software Infrastructure overview

Availability

SBSI Numerics

Numerical algorithms and Frameworks for

- Global optimsation ✔-Sensitivity analysis

- Bifurcation analysis

Command line on local machine,Bluegene, or ECDF

Page 21: Systems Biology Software Infrastructure overview

Availability

Available for Windows XP/Vista, MacOSX10.5, 64bit Linux .

SBSI Visual

✔ Desktop application✔ Upload and edit SBML models✔ Run simulations✔ Interact with external repositories✔ Visualisation of data and results

Access to local or remote SBSINumerics

Page 22: Systems Biology Software Infrastructure overview

Availability

Deployed on SBSI server.

Access to test server, Bluegene

SBSI Dispatcher

(Task Manager)✔ Compile C codes✔Submit jobs to HPC

✔Retrieve results✔Provide job status

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