1. The Optimization Plug-in for the BioUML Platform E. O. Kutumova 1,2,*, A. S. Ryabova 1,3, N. I. Tolstyh 1, F. A. Kolpakov 1,2 1 Institute of Systems Biology, Novosibirsk, Russia; 2 Design Technological Institute of Digital Techniques SB RAS, Novosibirsk, Russia; 3 A.P. Ershov Institute of Invormatics Systems SB RAS, Novosibirsk, Russia; *Contacts: e.o.kutumova@gmail.com Motivation BioUML platform is the powerful tool for modeling of biological systems and their analysis. However optimization tools have been missed so far. Goals Multi-experiments fitting: – Several files with experimental datasets – Different initial states of the biological system Constraint optimization. Quickly find a solution of the optimization problems in particular using the parallel computations. A visual representation of data using analysis diagram Parameters optimization using Java Script. Results We developed optimization plug-in (fig. 1) for solving of non-linear optimization problems on biochemical pathways by minimization of a distance between model simulation results and experimental data. Optimization tools of BioUML are also available in the web edition of the framework (fig. 2). That means the user could perform the model optimization through the Internet via connection with the serve. Optimization methods: Adaptive simulated annealing; Cellular genetic algorithm; Evolution strategy with stochastic ranking (SRES); Particle swarm optimization; Deterministic global optimization method; Quadratic Hill-climbing. ODE solvers: JVODE – ported to Java version of CVODE; Dormand-Prince (explicit Runge-Kutta scheme); Imex (implicit–explicit Runge–Kutta scheme); Explicit Euler method. Distance functions: mean, mean square and standard deviation weight methods. Experiments types: time courses and steady states. Experimental data: Exact values of substance concentrations; Percentage values (relative to initial or completion values). Performance was compared to COPASI* software which is the most famous application for simulation and analysis of biochemical networks and their dynamics. In the case of a single-core computer BioUML finds comparable solutions three times faster then COPASI. Availability Home page: http://www.biouml.org/ Web edition: http://server.biouml.org/webedition/ http://79.125.109.165/bioumlweb/ Acknowledgments Part of this work was partially supported by European Committee grants №037590 “Net2Drug” and №202272 “LipidomicNet”. Simulation results for all experiments Optimization document Fitted values for two estimations MethodBioUML (4 cores) BioUML (1 core) COPASI (1 core) Evolutionary Programming ––77 – 118 sec Particle swarm6 – 8 sec15 – 23 sec67 – 92 sec SRES5 – 8 sec22 – 24 sec65 – 85 sec Cellular genetic algorithm 6 – 7 sec20 – 25 sec– Figure 1. Optimization plug-in implementation in BioUML Figure 4. Analysis diagram for the optimization procedure and results of the parameters for two experimental datasets simultaneously Figure 3. Java Script functions available for analysis in BioUML workbench or from the command line Table 1. Comparative statistics with COPASI [1] software (10 independent experiments, 10 000 simulations) *Hoops S., et al. (2006) COPASI – a complex pathway simulator. Bioinformatics, 22, 3067–3074. Figure 2. Web implementation of the optimization plug-in Optimize model represented in SBML format with SBGN notation Experimental data