Kinetic modeling of complex biochemical systems is central to the emerging field of systems biology. Kinetic models require definition of numerous free parameters, usually obtained by calibration to experimental data, that specify initial species concentrations and kinetic rate constants. Once calibrated, a model should be analyzed for its sensitivity and predictive power over ranges of parameter values. Both model calibration and analysis can require thousands to millions of model simulations for statistical convergence and significance. In many cases, the computational expense of simulation at this scale makes detailed model analysis infeasible.
Allows the study of selection pressures on genomic architecture. Aevol is an individual-based model of evolution. It is based on genetic algorithms and individual based modeling and permits to capture the evolutionary process. The tool allows to live the individuals on a toroidal, two-dimensional square grid, with each location being occupied by exactly one individual. It leads to the usual cooperation dilemma.
Allows biological systems modelling. JWS Online (Java Web Simulation) is a web server which permits to construct, modify and simulate kinetic models and also to store curated models. User can build and annotate their own model using the JWS Online simulator. Three types of analyses are available: a time simulation, steady-state analysis and metabolic-control analysis. The software is SBML compliant and can be useful for the study of cellular systems.
Allows simulation of movements and reactions of molecules within and between cells. MCell permits simulation of specific instance of chemical signaling, diffusion, and reaction of discrete neurotransmitter molecules in synaptic spaces. It can be useful for Monte Carlo simulation of diffusion in a volume or on a surface. This tool employs a Model Description Language (MDL) to define simulation input conditions and user-requested output.
Defines the water relations and transpiration of the leaf using the molecular mechanics of ion transport, metabolism, and signaling of the guard cell. OnGuard was developed for dynamic modeling of the guard cell. The software encompasses guard cell transport, signaling, and homeostasis and models guard cells across species, including those of Arabidopsis thaliana. It enables flexibility in model design for guiding experimentation, reformulating, and validating predictions across scales.
A software application for simulation and analysis of biochemical networks and their dynamics. COPASI is a stand-alone program that supports models in the SBML standard and can simulate their behavior using ODEs or Gillespie's stochastic simulation algorithm; arbitrary discrete events can be included in such simulations.
Assists users with the simulation of detailed spatio-temporal mechanisms of dynamic processes in the cell. ReaDDy is a particle-based reaction-diffusion simulation package suited for crowded cellular environments. This application is based on an open architecture design that enables existing particle simulation packages to be included as modules. It consists of three main components: the simulation engine, the input module that splits input information into an only particle and the output module based on a runtime analyzer scheme.
Allows users to mathematically model dynamical systems. PottersWheel is a comprehensive framework for data-based modelling in Systems Biology comprising multi-experiment fitting in normal and logarithmic parameter space. The software integrates statistical tests for model-data-compliance, model discrimination and identifiability analysis for non-linear relationships between an arbitrary number of parameters.
A text-based model definition language originally based on Jarnac, and extended to be fully modular. Antimony models can be converted to and from SBML, flattening the modularity in the process. The libAntimony library allows other software packages to import these models and convert them either to SBML or their own internal format.
A MATLAB environment to solve mathematical optimization problems which in dynamic modeling and control of biological systems. AMIGO2 is organized in four main modules: the pre-processor, the numerical kernel, the post-processor and the module of main tasks. It covers the iterative identification of dynamic models, it allows using optimality principles for predicting biological behavior and it deals with the optimal control of biological systems using constrained multi-objective dynamic optimization. It supports general non-linear deterministic dynamic models and black-box simulators, dealing with ordinary, partial or delay differential equations. AMIGO also offers various methods to analyze model identifiability: (i) local and global parametric sensitivities; (ii) the Fisher Information Matrix for an asymptotic analysis; (iii) cost contour plots and (iv) a robust Monte-Carlo sampling approach.
It is designed for computationally efficient and user-friendly integration of complex experimental data into models consisting of coupled non-linear ordinary differential equations (ODE). The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications.
A graphical modeling system for multi-agent based simulation of tissue homeostasis. An editor allows the intuitive and hierarchically structured specification of cellular behavior. The models are then automatically compiled into highly efficient source code and dynamically linked to an interactive graphical simulation environment. The system allows the quantitative analysis of the morphological and functional tissue properties emerging from the cell behavioral model.
Simulates systems at multiple scales using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch.
A problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client-server system, with more than a thousand world-wide users.
Designs and analyzes complex genetic circuits. iBioSim was developed to analyze biochemical reaction network models. It also supports abstraction-based analysis of these models and design of synthetic genetic circuits. This application includes project management features and a graphical user interface (GUI) that facilitate the development and maintenance of models as well as experimental and simulation data records.
A tool for evolving and designing biochemical reaction networks using genetic algorithm (GA). Typically, a BioJazz user wishes to evolve or design a small network or motif which accomplishes a specific function, such as a switch or an oscillator module. The network comprises a set of proteins whose attributes are encoded in a network's ''genome''. The ''genome'' is a binary string which contains all the information necessary to determine how many proteins are present in the network, their structure, which proteins interact and the biochemical parameters of their interaction.
A structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). CellDesigner supports simulation and parameter scan by an integration with SBML ODE Solver, SBML Simulation Core and Copasi. By using CellDesigner, you can browse and modify existing SBML models with references to existing databases, simulate and view the dynamics through an intuitive graphical interface.
Permits users to derive both a network of reactions and its kinetic parameters from reactive molecular dynamics (MD) simulations. ChemTraYzer is a methodology for deducing quantitative reaction models by identifying, quantifying, and evaluating elementary reactions of classical trajectories. It also includes an extension that can be used as a basis for methodological advancement of chemical kinetic modeling schemes and as a black-box approach to generate chemical kinetic models.
An extendable research tool for the numerical analysis and investigation of cellular systems. For a network of coupled reactions PySCeS does a stoichiometric matrix analysis, calculates the time course and steady state, and does a complete control analysis. It is extremely flexible and user-extensible.
Enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM3E establishes metabolite use requirements with metabolomics data, uses modelpaired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. It was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. GIM3E requires a cellular objective and condition-matched omics datasets, preferably transcriptomics data with good coverage of model genes and metabolomics data that may be qualitative or semi-quantitative in nature.