Unlock your biological data

?

Try: RNA sequencing CRISPR Genomic databases DESeq

Unclassified software tools | Mathematical modeling

1 - 50 of 63 results
filter_list Filters
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 50 of 63 results
FEBiO / Finite Elements for Biomechanics
Offers a nonlinear finite element solver for biomechanical applications. FEBiO provides modeling scenarios, constitutive models and boundary conditions. The application supplies algorithms to model tied or sliding contact under large deformations between elastic, biphasic, or multiphasic materials. Users can run five different types of analysis: (i) nonlinear elasticity and viscoelasticity; (ii) rigid body mechanics; (iii) multiphasic mechanics; (iv) interstitial growth mechanics and (v) heat conduction.
GenSSI / Generating Series for testing Structural Identifiability
Allows structural identifiability analysis of models describing biological systems under multiple experimental conditions. GenSSI is a software toolbox that performs structural identifiability analysis of linear and nonlinear ordinary differential equation (ODE) models. The software provides a Systems Biology Markup Language (SBML) import, automatic methods for multi-experiment structural identifiability analysis, and methods for the transformation of models. It permits users to obtain subsets/combinations of structurally identifiable parameters, which can be used to reformulate the model.
Tellurium
Allows building, analysis, simulation and reproducibility of models in systems and synthetic biology. Tellurium is an integrated development environment (IDE) that comes with several pre-installed Python libraries, plugins, and tools tailored for biological simulation. The software also includes plugins and modules ranging from visualization tools to algorithms for bifurcation analysis and multidimensional parameter scanning. It supports the COMBINE archive, a file format containing all the information needed to reproduce a simulation experiment.
SBSI / Systems Biology Software Infrastructure
Affords a suite for model analysis and data integration. SBSI is composed of three major software: (i) SBSINumerics, containing a library with parallelized implementations of global optimization algorithms; (ii) SBSIDispatcher, providing access to back-end HPC machines; and (iii) SBSIVisual, a client application to configure parameter optimization jobs, to link with external databases and to view the results of optimizations. There are no dependencies between modules.
PyDREAM
Simplifies parameter inference for complex biological systems. PyDREAM is an open-source implementation of the DREAM(ZS) and MT-DREAM(ZS) sampling algorithms for efficient inference of complex, high-dimensional, and posterior parameter distributions. It uses a common multi-chain architecture and creates candidate points in each chain on the fly via differential evolution. This tool focuses on three points: (1) specification of the prior parameter distribution, (2) selection of an appropriate likelihood function, and (3) Markov chain Monte Carlo (MCMC) simulation.
MAGPIE / Modeling and Analysis Generic Platform with Integrated Evaluation
Offers a platform dedicated to models’ community management. MAGPIE is accessible through three different ways: (i) a demo platform hosted on a web application; (ii) a source code allowing a local installation; and (iii) a R API. The application provides a repository of models and analysis tools that can be freely customized by multiple users. It also includes additional features for easing the visualization of results and a sharing results system based on twitter's hashtags model.
fluEvidenceSynthesis
Assists users in simplifing forward modelling of the effect of different intervention options. fluEvidenceSynthesis is a package that allows users to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. It is based on a method to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources.
GMDPtoolbox
Serves for modeling spatial management problems, for designing and analyzing policies and for comparing given policies by simulation. GMDPtoolbox is a toolbox dedicated to the Graph-Based Markov Decision Process (GMDP) framework. It is composed of two algorithms providing local policies by approximating the optimal solution of GMDP. This toolbox offers user a structure to encode GMDP problems, as well as modeling tools, solution algorithms, and analysis tools for evaluating and comparing policies.
SSCC TD Simulator / Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model simulator
Provides a model for representing compound stimulus configurations in a real-time architecture. SSCC TD Simulator is an extension of the Temporal Difference model which can be used for determining the hypothetic compound representation which can result from two or more stimuli that coexist according to specific conditions. It is also able to represent the formation of a compound representation of serial stimulus compounds.
NetLogo
Offers a modeling environment for simulating complex phenomena. NetLogo is available through both a standalone software and a web application coupled to a homepage that displays a library of models and extensions. The software encompasses a large model application area including spread of disease, fractals, cellular automata or crystallization. Additionally, the program includes a feature for saving models as Java applets, for facilitating the publication of the simulations from the tool to a Web page.
DBSCAN / Density-Based Spatial Clustering of Applications with Noise
Assists in clustering spatial data. DBSCAN is a non-parametric, density based clustering technique. It provides features that useful when detecting objects/class/patterns/structures of different shapes and sizes. This algorithm is a good candidate to find ‘natural’ clusters and their arrangement within the data space when they have a comparable density without any preliminary information about the groups present in a data set.
REANIMATE / REAlistic Numerical Image-based Modelling of biologicAl Tissue substratEs
Conducts realistic computational experiments that naturally incorporates the variability and heterogeneity found between biological samples. REANIMATE consists of two steps: (1) a solution is sought from a set of coupled fluid dynamics models that describe steady-state vascular and interstitial fluid transport; and (2) the steady-state solution is used to parameterize a time-dependent model that describes the vascular and interstitial uptake of exogenously administered material.
gfit
Allows analysis of many types of experiments, validation of computational models, and extraction of maximum information from the available experimental data. gfit aims to connect models with various types of experimental data. The software (1) simplifies the model's task of directly simulating experimentally observable variables, (2) maintains communications between the analysis components, acting as a mediator during regression analysis, and (3) facilitates customization of the analysis procedure.
APMonitor / Advanced Process Monitor
Permits users to optimize mixed-integer and differential algebraic equations. APMonitor is able to: (1) construct model, (2) fit parameters to data, (3) optimize over a future predictive horizon, and (4) transform differential equations into sets of algebraic equations. It employs a model to byte-code and is based on analysis of the sparsity structure of the model. This tool is able to solve the differential and algebraic equations using a simultaneous or sequential solution approach.
MUME / MUlti-Module Environment
Provides a classifier for abnormal heart rhythms (arrhythmia). MUME provides a multi-net system for the training and simulation of multi-net multi-architecture artificial neural systems. In this modular environment, multiple-net and multiple-algorithms can be used and combined with non-neural information processing systems. It also supports dynamic and static neural networks. It includes a timing classifier, a morphological classifier, a winner-take-all classifier and the X out of Y classifier.
0 - 0 of 0 results
1 - 2 of 2 results
filter_list Filters
thumb_up Fields of Interest
public Country
language Programming Language
1 - 2 of 2 results