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References:

(Kitano, 2002) Computational systems biology. Nature.

(Le Novere, (2015) Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet.

(Fisher and Henzinger, 2007) Executable cell biology. Nat Biotechnol.

(Eydgahi et al., 2013) Properties of cell death models calibrated and compared using Bayesian approaches. Mol Syst Biol.

(Gutenkunst et al., 2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol.

Source text:

(Harris et al., 2017) GPU-powered model analysis with PySB/cupSODA. Bioinformatics.

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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.

Allows users to simulate stochastic processes and ordinary differential equations (ODE) models.

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.

Assists COPASI users in the task of modelling biological systems. PyCoTools provides an interface to COPASI tasks with an emphasis on model calibration. The software supports a range of tools which are either wrapperd around COPASI tasks, an ordered workflow of task configurations, or plotting facilities for exploratory data analysis on parameter estimation data. It can improve the effectiveness with which one can calibrate models to experimental data and discriminate between alternate hypotheses.

A programming library for symbolic and numerical analysis of chemical reaction network models encoded in the Systems Biology Markup Language (SBML). The package employs libSBML structures for formula representation and associated functions to construct a system of ordinary differential equations, their Jacobian matrix and other derivatives. All functionalities of the library are implemented in several well-documented example programs and a simple command-line application with additional visualization modules.

Designs formal languages for describing qualitative or quantitative models of biochemical systems. BIOCHAM is based on a s rule-based language and on a temporal logic language. It permits continuous or stochastic simulations, and model validation or revision with respect to a formal qualitative or quantitative specification. This tool can be provided with various types of information, qualitative or quantitative, that can be found in the literature.

Generates automatically kinetic rate equations for metabolic and gene-regulatory networks given in SBML format. SBMLsqueezer is a software package designed for rapid, consistent prototyping of large-scale biochemical kinetic models. It can easily be integrated into versatile model construction workflows. This software aids the user in the model construction process by applying several criteria to automatically suggest appropriate equations for each reaction.

A modeling environment for the simulation and integration of cell-based models with ordinary differential equations and reaction-diffusion systems. It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code. Its graphical user interface supports the entire workflow from model construction and simulation to visualization, archiving and batch processing.

Provides features for parameter estimation problems. SBaddon is an extension to the Systems Biology Toolbox for MATLAB. The software aims to exemplify how the modular concept of the SBtoolbox can be used to make new functionality available without a lot of overhead. The main feature of SBaddon is the automatic conversion of SBmodels to compiled and executable MATLAB (MEX) functions. It also allows identifiability analysis and provides optimization algorithms.

An extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language (SBML). SBML is the most widely used standard for representing dynamic networks, especially biochemical networks. libRoadRunner is fast enough to support large-scale problems such as tissue models, studies that require large numbers of repeated runs and interactive simulations. libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python interface. libRoadRunnerâ€™s speed and ease of integration allow researchers to solve very large models, include models embedded in multi-scale systems and run large ensembles of smaller models.

Offers a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. Cellware uses a proprietary file format (Cellware model or CWM) that stores information pertaining to the model and the corresponding simulation environment. Cellware can also import models from the (System Biology Mark-up Language SBML) format.

Allows computation of optimal experiments for biochemical kinetic systems with underlying ordinary differential equation (ODE) models. ModelDiscriminationToolkit is the implementation of a model discrimination algorithm inspired by the demands of biological experimentalists who perform one run measurement where perturbations to the system are possible.

A browser-based application that facilitates construction, simulation and analysis of kinetic models in systems biology. Thus, SYCAMORE allows e.g. database supported modelling, basic model checking and the estimation of unknown kinetic parameters based on protein structures. In addition, it offers some guidance in order to allow non-expert users to perform basic computational modelling tasks.

A user-friendly simulator of dynamic networks for constructing, visualizing, and analyzing kinetic models of biological systems. In addition to generic reaction networks, Dynetica facilitates construction of models of genetic networks, where many reactions are gene expression and interactions among gene products. Further, it integrates the capability of conducting both deterministic and stochastic simulations.

Intends for modern regression modeling and stands in-between classical generalized linear and additive models. Mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing component wise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

An integrated simulation environment for managing quantitative and qualitative information on cellular networks, and for interactively exploring their steady-state and dynamic behaviors over the web. A user-friendly web interface allows users to efficiently create, visualize, simulate and store their reaction network models, thereby facilitating kinetic modeling and simulation of biological systems of interest. Supported analysis methods for such models include, but not limited to, structural pathway analysis, metabolic control analysis (MCA), conservation analysis and dynamic simulation. A variety of model collections publicly available have been compiled to provide comprehensive implications for cellular dynamics of the models.

A library for simulating an SBML model which contains ordinary differential equations (ODEs). LibSBMLSim provides simple command-line tool and several APIs to load an SBML model, perform numerical integration (simulate) and export its results. Both explicit and implicit methods are supported on libSBMLSim.

Allows analysis of complex biological reaction networks. IBRENA is a software package coupling forward sensitivity analysis with multivariate analysis techniques, especially principal component analysis (PCA) and singular value decomposition (SVD). The software features adjoin sensitivity analysis and model reduction methods and a GUI with built-in plot panels for rapid visualization and interpretation of results.

Aims to provide an SBML parser and library that maps all SBML elements to a flexible and extended type hierarchy. JSBML is a community-driven project to create a free, open-source, pure Java library for reading, writing, and manipulating SBML files and data streams. It is an alternative to the mixed Java/native code-based interface provided in libSBML. Where possible, it strives to attain 100 per cent API compatibility with the libSBML Java API, to facilitate a switch from one library to the other.

Allows users to approximate the ordinary differential equation (ODE)-based biopathway dynamics. PADA is a graphical processing unit (GPU)-based implementation for constructing the dynamic Bayesian network (DBN) approximations. The software has been tested on ODE models of the epidermal growth factorâ€“nerve growth factor (EGFâ€“NGF) pathway, the segmentation clock network and the thrombin-dependent MLC phosphorylation pathway.

Validates the ever-growing biological pathway simulation modelsâ€”both in complexity and quantity. MIRACH is an on-the-fly probabilistic model checker for quantitative pathway models. It supports popular formats such as SBML and CSML. This quantitative model checker can be a valuable addition to the available arsenal of qualitative (GNA) and rule-based (BioLab) model checkers. Major contributions include a model checker to be integrated with the HFPNe simulation engine, an expressive and powerful Petri net framework for defining biological pathway models.

Supports users in generating, modifying, simulating, and exporting standard compliant simulation experiments. SED-ML Web Tools implement all current SED-ML specifications and, thus, support complex modifications and co-simulation of models in SBML and CellML formats. It provide an easy-to-use wizard to generate SED-ML files for a model encoded in the SBML format. SED-ML Web Tools supports researchers in developing simulation studies that comply with the SED-ML standard.

Takes ordinary differential equation (ODE) models written in MATLAB and export them as SBML files. MOCCASIN aims to facilitate scripting and automation. The tool offers (1) a module that parses MATLAB files; (2) a module that extracts the ODE-based model and produces a model with explicit ODEs; (3) a module that infers the biochemical reactions implied by the ODEs and produces SBML output with biochemical reactions for kinetics; (4) a command line interface and (5) a graphical user interface (GUI).

A web-based application for sensitivity analysis of mathematical models. The sensitivity analysis is based on metabolic control analysis, computing the local, global and time-dependent properties of model components. Interactive visualization facilitates interpretation of usually complex results. SensA can contribute to the analysis, adjustment and understanding of mathematical models for dynamic systems.

Allows graphics processing unit (GPU)-based kinetic simulations. PySB/cupSODA is a high-performance GPU-based kinetic simulator for the modeling community. It can run thousands of parallel simulations on a common desktop workstation. The tool aims to accelerate and streamline the process of analyzing complex biochemical models for systems biology applications. It is based on a system of ordinary differential equations from a reaction-based mechanistic model.

Allows users to sample ordinary differential equation (ODE) model parameters. MCMC_CLIB is an implementation of SMMALA (simplified manifold Metropolis-adjusted Langevin algorithm), a sampling algorithm. The approach is motivated by modeling practices in systems biology, where model parameters are typically required to be of fixed sign for model stability. An important feature is the ability of the implemented likelihood function to deal with relative data like Western blots in arbitrary units.

Forecasts parameters in biological dynamical systems. ShinyKGode is an R package, derived from the KGode package, composed of four main features: (i) an interface that run gradient matching to solve ordinary differential equations, (ii) a selection of three standard benchmark models, (iii) the possibility to import user-defined differential equations and (iv) a panel that allows users to visualize plots for evaluating convergence.

Allows to manage global stochastic studies. SpaceScanner supports: (1) parallel optimization runs with automated recognition of consensus and stagnation situations, (2) automatic switching between different user-selected global stochastic optimization methods in case of stagnation in the current method, (3) determination of the best sets of adjustable parameters for a pre-set range of a number of adjustable parameters in combination, and (4) search for the minimal number of adjustable parameters that can reach the requested fraction of the total optimization potential.

Allows users to perform noise decomposition in stochastic biochemical systems. StochDecomp permits researchers to compute contributions of individual reactions to the total variability of a systemâ€™s output. It is able to dissect noise propagation through biological systems and enables to understand the role of noise in function and evolution. In summary, it assists biologists to either harness or dampen the effects of noise in molecular signaling and response networks.

Constructs abstractions using information Dynamic Bayesian Network (DBN). DBNizer reduces the number of variables from 92 to 10, and accelerates numerical simulation by an order of magnitude, yet preserving essential features of cell death time distributions. It generates a large number of trajectories of the underlying biochemical model by numerical integration of the original model. The tool selects the suitable subset of variables to be used for DBN construction and infers their edge relationships.

A MATLAB toolbox for the identification of parameters and parameter ensembles of ODE models from time-series data. The tools within REDEMPTION are accessible through a user-friendly MATLAB UI and applicable for ODE models with power-law or lin-log kinetics, and those in SBML format.

Provides algorithms to i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and ii) perform reasoning and inference on the learned Bayesian Networks. Bnstruct can handle both discrete and continuous variables in the dataset manipulation and imputation and infer the estimated probability distribution of some variables. It also contains methods for learning using the Bootstrap technique.

Provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats and helps in the selection of input features. It provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for Markov (state) models (MSMs), hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. Plotting functions to produce a manuscript-ready presentation of the results are available.

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