Transcript BioUML integrated platform for building virtual cell and

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BioUML
ISB
integrated platform for building
virtual cell and
virtual physiological human
Fedor Kolpakov
Institute of Systems Biology
Laboratory of Bioinformatics,
Design Technological Institute of Digital Techniques
Novosibirsk, Russia
BioUML platform
• BioUML is an open source integrated platform for systems biology
that spans the comprehensive range of capabilities including access
to databases with experimental data, tools for formalized
description, visual modeling and analyses of complex biological
systems.
• Due to scripts (R, JavaScript) and workflow support it provides
powerful possibilities for analyses of high-throughput data.
• Plug-in based architecture (Eclipse run time from IBM is used)
allows to add new functionality using plug-ins.
BioUML platform consists from 3 parts:
• BioUML server – provides access to biological databases;
• BioUML workbench – standalone application.
• BioUML web edition – web interface based on AJAX technology;
BioUML workbench
http://www.biouml.org/
BioUML web edition
Availability
http://www.server.biouml.org/bioumlweb
BioUML architecture
Plug-in based architecture
A plug-in is the smallest unit of BioUML workbench function that can be developed
and delivered separately into BioUML workbench. A plug-in is described in an XML
manifest file, called plugin.xml. The parsed contents of plug-in manifest files are made
available programmatically through a plug-in registry API provided by Eclipse
runtime.
- extension points are well-defined function points in the system where other
plug-ins can contribute functionality.
- extension is a specific contribution to an extension point. Plug-ins can define
their own extension points, so that other plug-ins can integrate tightly with them.
Plug-in
- plugin.xml
- Java jar files
Plug-in
- plugin.xml
- Java jar files
Eclipse platform runtime (IBM)
Plug-in
- plugin.xml
- etc.
Standard module
GeneNet module
Diagram types
- Semantic map
- Pathway
- Pathway simulation
Database
Database adapter
KEGG/pathways
module
TRANSPATH
module
Java objects
Gene
Protein
Query engine
…
SBML module
Diagram
view part
Meta model
Graph structure
ModuleType
DiagramType
Executable model
-diagram types
-data categories
-query engine
-semantic controller
-diagram view builder
-diagram filter
Diagram
editor
Eclipse platform runtime
Analysis
tools
Diagram
editor part
Simulation
tools
Other
tools
Workbench UI
Perspectives
Views,
editors
Menus,
toolbars, etc.
BioUML main features
• Supports access to main biological databases:
– catalolgs: Ensembl, UniProt, ChEBI, GO…
– pathways: KEGG, Reactome, EHMN, BioModels, SABIO-RK,
TRANSPATH, EndoNet, BMOND…
• Supports main standards used in systems biology:
SBML, SBGN, CellML, BioPAX, OBO, PSI-MI…
• database search:
– full text search using Lucene engine
– graph search
• graph layout engine
• visual modeling:
– support for hierarchical models;
– simulation engine supports (ODE, DAE, hybrid, stochastic, 1D PDE);
– parameters fitting;
• genome browser (supports DAS protocol, tracks import/export);
• data analyses and workflows – specialized plug-ins for microarray
analysis, integration with R/Bioconductor, JavaScript support, interactive
script console.
BioUML
web edition
http://server.biouml.org/bioumlweb
Text search
Metaphor: biological systems reconstruction
as solitaire (patience) game
Desk – BioUML editor
Solitaire – biological pathway
Cards – biological objects
(genes, proteins, lipids, etc.)
Pack of cards – different
biological databases
Graph layout
Visual modeling
Pane: model parmaters
Pane: model variables
Pane: model variables
Pane: model simulation
Reports (templates)
Parameters fitting
Main features
• Experimental data – time courses or steady
states expressed as exact or relative values of
substance concentrations
• Different optimization methods for analysis
• Multi-experimentsfitting
• Constraint optimization
• Local/global parameters
• Parameters optimization using java script
Parameters fitting – usser interface
Comparison with COPASI
Method
Evolutionary
Programming
Particle swarm
BioUML
(4 cores)
–
7,1 sec
7,7 sec
6,9 sec
(10,000 simulations)
BioUML
(1 core)
–
COPASI
(1 core)
1 min 58,2sec
1 min 31,3 sec
1 min 16,6 sec
22,4 sec
15,3 sec
22,5 sec
1 min 32 sec
1 min 26,4 sec
1 min 07,1 sec
Stochastic
7,5 sec
Ranking Evolution 7,47 sec
Strategy
6,9 sec
23,4 sec
23,5 sec
22,2 sec
1 min 25,0 sec
1 min 5,6 sec
1 min 8,8 sec
Cellular genetic
algorithm
25,5 sec
22,1 sec
20,8 sec
7,7 sec
7,5 sec
7,2 sec
–
Genome browser
Genome browser: main features
• uses AJAX and HTML5 <canvas> technologies
• interactive - dragging, semantic zoom
• tracks support
• Ensembl
• DAS-servers
• user-loaded BED/GFF/Wiggle files
F ile s equence
(F AS T A/E MB L /etc.)
D AS s ource:
E ns embl
…
L ocal
E ns embl D B
D AS s equence
E ns embl s equence
V arious s equence and
track (s ites collection)
s ources are
acces s ible via unified
interfaces making it
eas ier to us e different
data s ources .
Analys is res ult:
T F B S prediction analys is ,
MAC S analys is , …
D AS track
chr1
chr1
chr1
chr1
…
WEB-BASED
GENOME
BROWSER
USING AJAX
AND CANVAS
TECHNOLOGIES
233604
559767
742600
742600
233639
559802
742635
742635
S Q L T rack (table
in local databas e)
E ns embl track
T rack interface
S equence interface
…
C onvert reques ted
fragment to V iew
C onvert reques ted fragment to V iew
O ther graphical objects like
diagrams can be rendered
into V iew and dis played on
the web us ing C anvas as
well. C reated V iew can
als o be us ed to paint its elf
on s tandalone client or
exported into various
ras ter or vector image
formats .
Poster
View (J ava)
S equence view
C ompos iteV iew (ruler)
L ineV iew
L ineV ie
w
L ineV ie
w
T extV iew (s equence)
ACGTACGTACGT…
T rack view
C ompos iteV iew (s ite)
B oxV iew
(s ite body)
…
T extV iew
(s ite name)
C ompos iteV iew (s ite)
B oxV iew
(s ite body)
…
T extV iew
(s ite name)
View (s erver)
D AS s ource:
UC S C
Us er-loaded
B E D /G F F /Wiggle
files
Model
Internet s ources
S erializ e to J S O N and trans fer to client (gz ipped)
T he s ame V iew hierarchy
is recreated in client
J avaS cript code from
J S O N received from the
s erver. If V iew was
already created on client,
partial updates of changed
elements are als o
s upported.
{"children":[{"children":[{"pen":{"color":[0,0,0,255],"width":1},"height":54,"trackWidth":665,"width":66
9
,"class":"TrackBackgroundView","type":"0","y":10,"x":2},{"model":"3596179","children":[{"pen":{"color":[
0,255,0,255],"width":5},"y1":31,"y2":31,"class":"LineView","x2":664,"type":"0","x1":1},{"alignment":0,"t
e
xt":"...dust...","font":{"color":[0,0,0,255],"font":["Serif",0,12]},"class":"TextView","type":"0","y":23
,
"x":313}],"class":"CompositeView","type":"20"},...
D es erializ e
View (J avaS cript)
S equence view
C ompos iteV iew (ruler)
L ineV iew
L ineV ie
w
L ineV ie
w
…
T extV iew (s equence)
ACGTACGTACGT…
T rack view
C ompos iteV iew (s ite)
B oxV iew
(s ite body)
T extV iew
(s ite name)
C ompos iteV iew (s ite)
B oxV iew
(s ite body)
D raw to <canvas >
T extV iew
(s ite name)
…
View (client — Web-brows er)
J S O N-encoded view
T.F. Valeev,
N.I. Tolstykh,
F.A. Kolpakov
Data analyses
JavaScript host objects allows
to merge R/Bioconductor and Java/BioUML worlds
R world
Java/BioUML world
Analysis workflow
POSTER:
MICROARRAY DATA ANALYSIS PLUGIN FOR BIOUML
I.N. Kiselev, A.A. Shadrin,Y.V. Kondrakhin, F.A. Kolpakov
Virtual physiological human
Virtual physiological human
The virtual physiological human (VPH) initiative is intended to support the
development of patient-specific computer models and their application in
personalised and predictive healthcare. The VPH, a core target of the European
Commission's 7th Framework Programme, will serve as a ‘methodological and
technological framework that, once established, will enable collaborative
investigation of the human body as a single complex system'
(http://www.europhysiome.org/roadmap/). As such, the VPH initiative constitutes
an integral part of the international Physiome Project
(http://www.physiome.org.nz/), a worldwide public domain effort to develop a
computational framework for the quantitative description of biological processes
in living systems across all relevant levels of structural and functional integration,
from molecule to organism, including the human (Kohl et al, 2000;
Bassingthwaighte et al, 2009).
Kohl P.,Noble D., 2009
(Systems biology and the virtual physiological human. Mol Syst Biol. 2009;
5:292.)
Virtual physiological human
simulation approaches
1. ODE (DAE, hybrid systems)
2. 1D PDE
arterial tree, cardio-vascular system
3. Agent based modeling
Models of regulation of blood pressure (Karaaslan et al, 2005)
Numerical analysis of complex model of human
blood flow circulation using 1D hemodynamic
model
010 010 00100 11101
IS B
T.I. Leonova1,2,5,*, E.A. Biberdorf 3,5, F.A. Kolpakov1,2, A.M. Blokhin3, 5, A.L. Markel4.
1Institute
of Systems Biology, Novosibirsk, Russia; 2Design Technological Institute of Digital Techniques SB RAS, Novosibirsk, Russia; 3Sobolev Institu
Mathematics SB RAS, Novosibirsk, Russia; 4Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia; 5Novosibirsk State University, Novosibir
Russia.
* Corresponding author: : [email protected]
Motivation
Results
High blood pressure – hypertension – is a state affecting
hundreds of millions of people worldwide and is a leading
cause of morbidity and mortality in developed countries. A
complex mathematical model of blood flow circulation has
been designed for the purpose of scientific research of
human essential hypertension progress.
Model of the human arterial system was created on t
base of 1D model of blood flow circulation in 55 ma
arteries using methods of lines and orthogonal sweep. Fi
1 demonstrates the user interface of BioUML plug“Hemodynamics”, which was created for work with 1
model. This model allows simulation of hemodynamics
arterial system with any number of vessels (arterial tr
should be binary). Numerical data of blood flow dynami
were obtained for all 55 arteries, as well as for cross-secti
of arteries according to cardiac contractions. Fig.
demonstrates pressure dynamics of carotid arteries
comparison with ascending aorta.[2]
Numerical model was verified by comparing Euler a
Runge-Kutta methods of integrating.
Tasks and Goals
1. Design of one-dimensional (1D) mathematical model of
human vascular system. Adaptation of 1D model to the
BioUML workbench for further work.
2. Verification and validation of one-dimensional (1D)
model of hemodynamics.
3. Development of a filtration block of the human blood
flow circulation model.
4. Development of a closed model of the human blood flow
circulation including a filtration block and a model of the
heart.
Methods
1. The model of the human arterial system was created as a
graph of 55 arteries (Lamponi, 2004 [1]).
2. The blood flow in an artery is described by onedimensional model. Methods of lines and orthogonal
sweep were used for calculations.
3. BioUML (http://www.biouml.org) technology was used
for formal description of CVS and development of
"Hemodynamics" plug-in that provides user friendly
Figure 1. User interface of plug-in “Hemodynamics” in BioUML
workbench. The vascular tree of 1D model.
The model was validated on the basis of researching
velocity of the pressure pulse wave. Constructed model
arterial system can reproduce adequately phenomenon
pulse wave.
Numerical modeling of an inflatable cuff effect was bas
on linear and nonlinear functions and has been designed f
purposes of validation of the model and researchi
Korotkoff sounds. The result of modelling shows th
Korotkoff sounds couldn’t be found in a case of circul
vessel section.
Model of blood filtration in tissues was created usi
Darcy’s law on a simple graph of six vessels (Fig. 3, Fi
4). Fig. 5 a), b) demonstrates pressure dynamics of 1, 2,
and 6 vessels in a case of Kf=0.00948 and Kf=0.0948.
Arterial system model was extended by attaching model
Agent based modeling
Agent based modeling
BioUML platform
• BioUML is an open source integrated platform for systems biology
that spans the comprehensive range of capabilities including access
to databases with experimental data, tools for formalized
description, visual modeling and analyses of complex biological
systems.
• Due to scripts (R, JavaScript) and workflow support it provides
powerful possibilities for analyses of high-throughput data.
• Plug-in based architecture (Eclipse run time from IBM is used)
allows to add new functionality using plug-ins.
BioUML platform consists from 3 parts:
• BioUML server – provides access to biological databases;
• BioUML workbench – standalone application.
• BioUML web edition – web interface based on AJAX technology;
Acknowledgements
Part of this work was partially supported by the grant:
European Committee grant №037590 “Net2Drug”
European Committee grant №202272 “LipidomicNet”
Integration and interdisciplinary grants №16, 91 of SB RAS.
Software developers
Biologists
Nikita Tolstyh
Alexey Shadrin
Ruslan Sharipov
Elena Kutumova Tatyana Leonova
Ivan Yevshin
Ilya Kiselev
Mikhail Puzanov
Tagir Valeev
Anna Ryabova