SimBiology in addition to MATLAB: A Software Plat as long as m as long as Modeling, Simulation in addition to Analysis

SimBiology in addition to MATLAB: A Software Plat as long as m as long as Modeling, Simulation in addition to Analysis

SimBiology in addition to MATLAB: A Software Plat as long as m as long as Modeling, Simulation in addition to Analysis

Zulaica, Don, Contributing Writer has reference to this Academic Journal, PHwiki organized this Journal SimBiology in addition to MATLAB: A Software Plat as long as m as long as Modeling, Simulation in addition to Analysis of Biological Systems Ricardo Paxson PRIME Workshop, Princeton University June 16, 2006 Pre-SimBiology Model in MATLAB Code Desired Software Features: Models are a Store of Institutional Knowledge Modularity Larger models can be built by merging or joining multiple simpler models Portability Easy to share models between researchers Supports various operating systems Encapsulation Models in addition to simulation data as well as annotation in addition to references to a knowledge base are kept in one package Versioning New model in as long as mation is easily incorporated into an older model Programmatic flexibility Models can be manipulated programmatically Scalability Use of hierarchy to ease the manipulation of large models Per as long as mance (Simulation, analysis, rendering, etc.)

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SimBiology 2.0 Key Features Deterministic in addition to stochastic simulation engines Graphical environment including a pathway editor User configurable units in addition to kinetic law libraries Models in addition to simulation results are saved in a project file Import in addition to export in SBML as long as mat Parameter estimation Computation of sensitivities Conserved moieties Ensemble runs Can be used with Distributed Computing Toolbox

Block appearance can be customized in addition to stored in a block library as long as subsequent use in addition to st in addition to ardization. Model with 1000 species in addition to 990 reactions

Here is an example from the MATLAB comm in addition to line: % Load a SBML model >> modelObj = sbmlimport(‘oscillator’) SimBiology Model – Oscillator Model Components: Models: 0 Parameters: 0 Reactions: 42 Rules: 0 Species: 23 MATLAB Comm in addition to line Adding a reaction to this model: % degradation of pA (protein A) >> r = modelObj.addreaction(‘pA -> null’); >> k = r.addkineticlaw(‘MassAction’); >> k.addparameter(‘k’,’Value’,3.4,’ValueUnits’,’1/second’); >> k.ParameterVariableNames = ‘k’; MATLAB Comm in addition to line – continued The MATLAB variable modelObj can be used to query, simulate or manipulate the model. % Simulate the model >> [t, x] = sbiosimulate(modelObj); >> plot(t, x) MATLAB Comm in addition to line – continued

Bridging the Gap Intuitive graphical user interfaces St in addition to ard graphical pathway representations Open environment in support of customization Programmatic access to software tools Changes in the Biological sciences curriculum Quantitative methods Programming Extra slides Drug Discovery with in silico design Patient Stratification Design Verification Parameter Estimation Compile Literature Knowledge Generate Experimental Data Model target in addition to associated pathways Model refinement, training, in addition to validation Identify target & best mechanism of action Drug C in addition to idate In vivo testing In vitro testing Model Elaboration, Refinement Verification Drug Development Strategy Sensitivity Analysis Test in addition to Verification

Sensitivity Analysis Complex-step method to compute derivatives Martins ACM Transactions on Mathematical Software, Vol. 29, No. 3, September 2003, Pages 245–262. Sensitivity Analysis example: >> m = sbmlimport(‘radiodecay’); >> [t, x, names] = sbiosimulate(m); >> plot(t,x); legend(names); Parameter Estimation Uses the optimization toolbox in addition to the GADS (Genetic Algorithms in addition to Directed Search toolbox). Algorithms used: Unconstrained nonlinear minimization (Nelder-Mead) Non-linear least squares Constrained minimization Pattern search Genetic algorithms Hybrids (pattern/GA in addition to gradient based methods)

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Parameter Estimation m = sbmlimport(‘radiodecay’); convertMA(m); m1 = copyobj(m); cs = m.getconfigset; cs.SolverType = ‘ssa’; cs.SolverOptions.LogDecimation = 1; cs.StopTime = 10; [et,ex] = sbiosimulate(m); plot(et,ex); drawnow; cs1 = m1.getconfigset; cs1.StopTime = 10; p = sbioselect(m1, ‘type’, ‘parameter’, ‘name’, ‘c1′); p.Value = 3; [t,x] = sbiosimulate(m1); hold plot(t,x,’b’); myopt1 = optimset(‘Display’,’iter’); [k, result] = sbioparamestim(m1, et, ex, {}, {‘c1’},{},{‘fmincon’, myopt1}); p.Value = k; [t,x] = sbiosimulate(m1); plot(t,x,’r’); Conserved quantities (Moieties) >> m = sbiomodel(‘cycle’); >> m.addreaction(‘a -> b’); >> m.addreaction(‘b -> c’); >> m.addreaction(‘c -> a’); >> sbioconsmoiety(m, ‘semipos’, ‘p’); ans = ‘a + b + c’

SimBiology Quick Overview So far it is used most frequently as long as modeling of Signal Transduction pathways Features: Graphical modeling environment Completely programmable in addition to integrated with MATLAB Graphical editor as long as model visualization in addition to construction Import in addition to export models using st in addition to ard as long as mats SBML in addition to Excel Stochastic Simulations Gillespie exact stochastic algorithm Explicit Tau leaping Implicit Tau leaping Deterministic Simulations Ordinary Differential Eqs (ODE) Differential Algebraic Eqs (DAE) Stiff solvers System can be specified as a list of Reactions Automatic graphical layout Dimensional analysis in addition to Unit conversion Searching tools Model analysis tools Parameter estimation Sensitivities Conserved Moieties Future Directions Modeling Model merging in addition to joining Rule based generation of reactions Transport processes PK/PD model integration (Simulink) Compartments / Submodels Simulation Distributed computing support Hybrid Stochastic & Determinisitc Parallel computing Analysis Bifurcation Analysis Parameter scans Graphical environment System can be specified as a list of reactions Choosing from a user defined library of kinetics produces the corresponding reaction rate.

Graphical environment Diagram icons are user customizable in addition to can be placed in libraries. This example library shows icons taken from Biocarta.

Zulaica, Don Contributing Writer

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