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Software for Modeling and Simulation of Biochemical Networks http://www.copasi.org http://www.copasi.org Stefan Hoops Virginia Bioinformatics Institute

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Overview Motivation COPASI Acknowledgement Other Software

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Biochemical Networks Data:Simple Network: A B C + D E B + 2 FG + H

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Modeling Paradigm Top Down Phenomenological modeling approach describing experimental data. Bottom Up Small well understood models, e.g. enzymatic reactions are used to comprise larger models.

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What we need? Easy to use analysis tools Interaction between tools Simulation with trusted results Parameter estimation capabilities Model comparison.

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COPASI Features Time Course Steady State Structural Analysis Metabolic Control Analysis Lyapunov Exponent Calculation Parameter Scan Optimization Parameter Fitting

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ODE Based Time Course Simulation -100 10-1 0-10 010 00-2 001 ABCDEFGHABCDEFGH................ = v 1 (A, B, H) v 2 (B, C, D, E) v 3 (B, E, F, G, H) x = N v with:. v = v 1 v 2. v m x = x 1 x 2. x n In general:

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Stochastic Time Course Simulation Initialize system Calculate: Reaction probabilities Generate random numbers to determine: time of next reaction which reaction happens Update the system the system Example

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Optimization Optimization attempts to maximize or minimize an objective function. Note, that the maximum of a function f is equivalent to the minimum –f Given a real-valued scalar function f(x,k) of n parameters k=(k 1,..., k n ) find a minimum of f(x,k) such that: g i (x) ≥ 0 with i=1,..., m (inequality constraints) h j (x) = 0 with j=1,..., m’ (equality constraints)

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Numerical Optimization Cycle

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Optimization Methods Gradient based Steepest Descent Levenberg Marquard Direct Deterministic Hooke & Jeeves Random Genetic Algorithm Evolutionary Programming Random Search Nelder Mead SRES Simulated Annealing

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Parameter Estimation / Fitting This is a case of optimization with a special objective function: The simulation results shall match the experimental results closely.

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Parameter Estimation Result

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Command Line Interface Suitable for long computational task like Optimization or Parameter Estimation Background progress for Web-applications or Web-services Basic usage: Create a model with the COPASI GUI Specify computational task in the GUI Save File “model.cps” CopasiSE “model.cps”

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Available Platforms Linux All WIN32 OS starting Windows 98 (Intel) Mac OS X (PowerPC and Intel) SunOS starting with Solaris 8 (sparc) Achieved through QT (Toolkit and libraries for GUI development) LAPACK / BLAS (matrix and vector routines) ODEPACK (ODE solver) EXPAT (XML library) LIBSBML (SBML library)

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Availability Current Release (June 2006) COPASI Version 4.0 Build 18 COPASI is publicly available since October 2004 (Build 9)

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Community Integration SBML import and export Berkeley Madonna export C source code generation

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Acknowledgements Mendes group @ VBI Pedro Mendes: Principal Investigator, occasional programmer, tester, and webmaster Sameer Tupe: Programmer (Fall 2004 - Fall 2005) Anurag Srivastava: Programmer (Fall 2004 - Summer 2005) Christine Lee: Programmer (Fall 2003 - Spring 2005) Gaurav Singh: Programmer (Fall 2003 - Spring 2004) Mrinmyee Kulkarni: Programmer (Spring 2002 - Fall 2003) Liang Xu: Programmer (Spring 2003 - Fall 2003) Mudita Singhal: Programmer (Spring 2002 - Summer 2003) Rohan Luktuke: Programmer (Summer 2002 - Fall 2002) Ankur Gupta: Programmer (Spring 2002 ) Wei Sun: Programmer (Fall 2001 - Summer 2002) Yonqun (Oliver) He: Programmer (Fall 2001 - Spring 2002) Aejaaz Kamal: Programmer (Spring 2001 - Summer 2001 ) Kummer group @ EML Research Ursula Kummer: Principal Investigator, tester Sven Sahle: Software architect, project manager, programmer Ralph Gauges: Software engineering, programmer, documentation Juergen Pahle: Programmer Natalia Simus: Programmer Jürgen Zobeley: Tester Ursula Rost: Programmer Katja Wegner: Tester, programmer, documentation Ralph Voigt: Documentation Sarah Lilienthal: Programmer (July - August 2005) Wenjun Hu: Programmer (August 2003 - October 2003) Carel van Gend: Programmer (October 2000 - May 2002)

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DOME DOME is a database and analysis system for functional genomics projects. It can be used to store and analyze transcriptomics, proteomics, and metabolomics data. The analysis that can be performed with DOME allow for an integrated view of the data generated using different technologies. We have implemented the system on three functional genomics projects on Medicago truncatula, Vitis vinifera and Saccharomyces cerevisiae and thus have attempted to make the system general enough to be used by various labs for their functional genomics needs.

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Overview of DOME Microarray 2D-PAGE Experiment metadata Statistical Analysis - Unsupervised (PCA, clustering) - Supervised (Discriminant analysis, GA-MDA, and others) Visualization - Biochemical Maps (using BROME) Data storage and processing Data analysis sp_summarySampling_point Sampling_replicate ma_normalizedprotein_normalizedmetabolite_normalized GC/MS; LC/MS; CE/MS gene protein compoundevent B-Net

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Multivariate Data Analysis for Genomics and Systems Biology Current analyses provided: correlation analysis partial correlation analysis principal component analysis (PCA), including biplot display linear multiple discriminant analysis (MDA), linear multiple discriminant analysis with genetic algorithm variable selection (GA-DFA) - 2 different algorithms. non-negative matrix factorization (NMF)

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