building virtual cell using biouml platform
DESCRIPTION
Building virtual cell using BioUML platform. Bios o ft.Ru. Fedor Kolpakov Biosoft.Ru, Ltd. Institute of Systems Biology, Ltd. Novosibirsk, Russia. July 6th - 11th 2013, St. Petersburg, RUSSIA. BioUML platform. - PowerPoint PPT PresentationTRANSCRIPT
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Building virtual cell using BioUML platform
Fedor Kolpakov
Biosoft.Ru, Ltd.Institute of Systems Biology, Ltd.
Novosibirsk, Russia
Biosoft.Ru
July 6th - 11th 2013, St. Petersburg, RUSSIA
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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;
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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:
– simulation engine supports (ODE, DAE, hybrid, stochastic, 1D PDE);– composite models;– agent based modeling, rule based modelling;– 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.
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Story 1:genome-scale model for prediction of synthesis rates of mRNAs and proteins
Initial data:Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J,
Wolf J, Chen W, Selbach M. Global quantification of mammalian gene expression control. Nature, 2011, 473(7347):337-342.
- mouse fibroblasts, parallel metabolic pulse labelling- simultaneously measured absolute mRNA and protein
abundance and turnover for 5000+ genes- first genome-scale quantitative model for prediction of
synthesis rates of mRNAs and proteins
Bukharov Aleksandr, Kiselev Ilya
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Experiment design
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Schwanhäusser B., et al., 20011 - Fig. 6:bComparison of synthesis rates of mRNA and proteins assuming the measured levels reflect averages over one cell cycle or steady-state values. For the synthesis rates of mRNA (light gray), the deviation between the two approaches is small, because mRNA half lives are mostly smaller than the cell cycle time. For protein synthesis (dark gray), the differences are substantial; they can differ for more than one order of magnitude.
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Schwanhäusser B., et al., 20011 - Fig. 6:bComparison of synthesis rates of mRNA and proteins assuming the measured levels reflect averages over one cell cycle or steady-state values. For the synthesis rates of mRNA (light gray), the deviation between the two approaches is small, because mRNA half lives are mostly smaller than the cell cycle time. For protein synthesis (dark gray), the differences are substantial; they can differ for more than one order of magnitude.
P – protein, exp – population mean,ss – steady state;
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“They do not take into account that gene expression in mammalian cells is non-continuous. In addition, the non-uniform age distribution of cells in culture as described in 19, 23 is neglected, since this effect is expected to be small compared to the deviation obtained by neglecting the cell cycle.“
Schwanhäusser B., et al., 20011, supplementary materials
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Agent based model
each cell is an agent
4247 blocks for protein synthesis
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Phase of a cellular cycle Percent of cells in phase
Rate of transcription
G1 50.8% Vsr
S 22.9% 0.7*Vsr G2 13% 2*Vsr M 3.3% 0 G0 10% Vsr
Tab.1. Parameters of a cellular cycle
Numerical experiment.
The initial size of population is 200 cells which divide within 108 hours. Average quantity of protein molecules were calculated. This experiment was repeated for 4247 proteins.
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Correlation of experiment and numerical modeling is equal to R=0.99
Absolute values also were coordinated (so for 81,6% of proteins absolute values differ by less than 7%
Main deviations from experimental values are observed for proteins with extremely low copy numbers, where experimental error can be significant.
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Used features of BioUML platform
• tables support (import, calculated columns);
• JavaScript API for model generation (from tabular data)
• visual modeling – diagram visualization
• agent based modeling
• ODE solver
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- tabIe with initial data (Schwannhauser B.et al., 2011);- calculated columns (k degradation)
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Java script for diagram generation
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Agent-based model
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Story 2: the modular model of apoptosis
Kutumova E.O. et. al., Advances in Experimental Medicine and Biology, 2012, 736(2):235-245
13 modules279 species372
reactions 459
parameters
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Modules: clear specification of interfaces
input/output
contacts
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Mitochondron module(BMOND ID: Int_Mitoch_module)
Bagci EZ, et al, Biophysical J 2006Albeck JG, et al, PLoS Biol 2008Additions:Activation of CREB and deactivation of BAD by Akt-PP and ERK-PPUpregulation of Bcl-2 by CREBBcl-2 suppression by p53
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EGF module(BMOND ID: Int_EGF_module)
Schoeberl B, et al: Nature Biotechnology 2002
Borisov N, et al: Molecular Systems Biology 2009Additions:Reactions of protein syntheses and degradations
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top-down
Modular model allows us to combine both up-down and bottom-up approaches
bottom-up
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Extreme programming (XP) Extreme programming (XP) methodologymethodology
•Planning– User stories– Make frequent small releases– The project is divided into iterations
•Coding– Code must be written to agreed standards– Unit test first– Integrate often – Collective ownership
•Testing– Unit/acceptance tests
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XP adaptation to modelingXP adaptation to modeling• Planning
– User stories: requirements for the model based on the experimental data
– Make frequent small releases– Iterations: after each iteration a new set of
experimental data should be reproduced• Coding
– Standards: SBGN, SBML, modular extension– Unit test first– Integrate often: model is saved into public
database– Collective ownership: collaborative editing
and chat• Testing
– Unit/acceptance tests: BioUML provides the facilities for testing the models.
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Execution phase of Execution phase of apoptosisapoptosis
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User stories: requirements for the User stories: requirements for the modelmodel
• No external signals concentration of the cleaved PARP is zero.
• CD95L signaling dynamics of pro-8 and casp-8 according to Bentele et. al.
• TNF-a signaling dynamics of pro-8 and casp-8 according to Janes et. al.
Iteration 1
Iteration 2
Iteration 3
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Types of the acceptance Types of the acceptance teststests
•Steady-state
•Time course
•Control of the variable values
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Iterations and acceptance Iterations and acceptance teststests
Iteration 1Iteration 2
Iteration 3
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Modeling of the execution phase of Modeling of the execution phase of apoptosisapoptosis
Bentele et. al., 2004
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Iteration 1: execution phaseIteration 1: execution phase
Steady-state acceptance test
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Iteration 2: CD95-signalingIteration 2: CD95-signaling
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Iteration 2: CD95-signalingIteration 2: CD95-signaling
Experiment time courses for pro-8 and casp-8 according to the data by Bentele, et. al., 2004
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Used features of BioUML platform
• visual modeling (SBGN notation)
• modular modelling
• parameters fitting
• acceptance tests
• ODE solver
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Parameters fitting – user interface
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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
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Bentele M, 2004
Neumann L, 2010
CD95L module and results of fitting its dynamics to
experimental data
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Story 3Predicting transcription level from ChIP-seq data
Ivan Yevshin, Dmitry Levanov, Yuriy Kondrakhin
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Initial row data, collected from literature, GEO, SRA and ENCODE databases were systematically collected and uniformly processed using specially developed workflow (pipeline) for BioUML platform:- sequenced reads were aligned to reference genome using Bowtie;- peaks were identified using MACS and SISSR algorithms- further refinement of obtained peaks- position weight matrices (PWM) were constructed by different methods(ChIPMunk, our own methods)- ROC curves were calculated to estimate and compare built PWM- site models (PWMs + thresholds) were constucted for recognition TFbinding sites.
TFClass database is used as a core for information about transcription factors, their classification and cross-linking with Ensembl.
BioUML platform provides web interface for access to GTRD database: search information, browsing, different data views. Built-in genomebrowser provides powerful visualisation of ChIP-seq data.
GTRD - Gene Transcription Regulation Database
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Prediction of gene expression level by ChIP-seq dataChIP-seq peaks (MACS) for histones and transcriptio factor binding sites were extracted from GTRD database for 2 cell lines: GM12878 and K562.
Machine learning - Random Forest algorithm.
R - 0.72 – 0.77
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Used features of BioUML platform• integration with Galaxy
to use 3-rd party command line tools (Bowtie, SISSR)• integration with R
for statistic analyses and plot visulisations• build-in analyses methods
NGS data control• workflows
for automated NGS data processing• genome browser
for ChIP-seq peaks visualisation• web interface for GTRD
– search possibilities– tree views– tables
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Galaxy – analyses methods
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Galaxy - workflow
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An example: workflow for analyses of ChIP-Seq data
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Genome browser
Two BAM tracks are compared with each other (Example view on Human NCBI37 Chr.1)Profile is visible showing the coverage
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Genome browser
Upon zooming individual reads become visible. All information associated with selected read is displayed in the Info box
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Genome browser
In detailed scale phred qualities graph is displayed along with changed nucleotides between read and reference sequence
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Story 4Glicomics – rule based modeling
Nikita Mandrik, Tagir Valeev
Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects.
Data and approach:
Bennun SV, Yarema KJ, Betenbaugh MJ, Krambeck FJ. Integration of the transcriptome and glycome for identification of glycan cell signatures.
PLoS Comput Biol. 2013;9(1):e1002813. doi: 10.1371/journal.pcbi.1002813.
BioNetGen – language and approach for rule based modeling
http://bionetgen.org
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Bennun S.V. et al., 2013
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Bennun S.V. et al., 2013
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Used features of BioUML platform• visual modeling• BioNetGen support (passed 8 available tests)• synchrinisation between model code and diagram• parameters fitting
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Collaborative research goals• Allow scientists to effectively work together even if they
are separated far away from each other• Help experimentalists and theorists to work together• Engage third-party experts
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Collaborative research1. Share
– Project data can be shared between several people– Changes are immediately visible by other participants– No need to ask administrator or somebody else to share the project
2. Communicate– Talk to each other in messenger– Conference chat
3. Collaborate– Edit together– Edit and chat– Review the changes of other participants
4. History– Monitor changes performed on the document– Rollback to older versions of the document– Compare different versions of the document– Project journal
5. Organize work (planned)– Create tasks (“works”) for other participants– Call for collaborators to solve particular problem– Review the results
6. Privacy– Your activity and data is invisible for people not belonging to your projects
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BioStorehttp://bio-store.org/
• BioStore is aimed to be the single place where researchers can register and obtain various products and services (either free or commercial).
• Having single BioStore account you may have an access to many BioUML servers, projects, downloads, etc.
Servers
DownloadsServices
Software
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Biostore
BioUML platform
Developers- plug-ins: methods, visualization, etc.- databases
Users- subscriptions- collaborative & reproducible research
Experts-services for data analysis- on-line consultations
BioUML ecosystem
provide toolsand databases
use provide services
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Challenges:
– methodology - how we can build, modify, verify and apply complex mathematical models for practical purposes;
– organization - how we can split main tasks into smaller, manageable parts and organize collaboration between researchers;
– software - we need a software platform that will provide necessary infrastructure for collaborative work of many researchers and will support needed methodology.
Building virtual cell
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Answers:
– methodology– modular modeling;– application of some practices from XP (eXtreme Programming):
– user stories, – iterative development, – acceptance tests;
– organization – Biostore - portal for collaborative research and crowd sourcing;
– software - BioUML platform - integrated open source platform for collaborative research in systems biology.
Building virtual cell
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www.biouml.org
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BioUML teamTagir Valeev Ivan Yevshin
Yuriy KondrakhinIlya Kiselev Dmitriy LevanovElena Kutumova Alexandr Bukharov Ruslan SharipovNikita Mandrik