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Virginia Bioinformatics Institute

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Presentation on theme: "Virginia Bioinformatics Institute"— Presentation transcript:

1 Virginia Bioinformatics Institute
Software for Modeling and Simulation of Biochemical Networks Stefan Hoops Virginia Bioinformatics Institute

2 Overview Motivation COPASI Acknowledgement Other Software

3 Biochemical Networks Simple Network: Data: A B C + D E B + 2 F G + H

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

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

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

7 ODE Based Time Course Simulation
-1 0 0 1 0 -1 0 -1 0 0 1 0 0 0 -2 0 0 1 A B C D E F G H . = 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:

8 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

9 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=(k1, ..., kn) find a minimum of f(x,k) such that: gi(x) ≥ 0 with i=1,..., m (inequality constraints) hj(x) = 0 with j=1,..., m’ (equality constraints)

10 Numerical Optimization Cycle

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

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

13 Parameter Estimation Result

14 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”

15 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)

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

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

18 Acknowledgements Mendes group @ VBI
Pedro Mendes: Principal Investigator, occasional programmer, tester, and webmaster Sameer Tupe: Programmer (Fall Fall 2005) Anurag Srivastava: Programmer (Fall Summer 2005) Christine Lee: Programmer (Fall Spring 2005) Gaurav Singh: Programmer (Fall Spring 2004) Mrinmyee Kulkarni: Programmer (Spring Fall 2003) Liang Xu: Programmer (Spring Fall 2003) Mudita Singhal: Programmer (Spring Summer 2003) Rohan Luktuke: Programmer (Summer Fall 2002) Ankur Gupta: Programmer (Spring 2002 ) Wei Sun: Programmer (Fall Summer 2002) Yonqun (Oliver) He: Programmer (Fall Spring 2002) Aejaaz Kamal: Programmer (Spring Summer 2001) Kummer 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 October 2003) Carel van Gend: Programmer (October May 2002)

19 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.

20 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_summary Sampling_point Sampling_replicate ma_normalized protein_normalized metabolite_normalized GC/MS; LC/MS; CE/MS gene protein compound event B-Net

21 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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