Model selection and parameter inference software tools | Mathematical modeling
The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions.
Implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. ABC-SysBio is designed to work with models written in systems biology markup language (SBML).
Allows communication between heterogeneous application components. SBW is a computational resource sharing framework that allows applications to communicate with each other efficiently and without losing their identity. Applications can be written in several different languages and can run on different operating systems across the internet.
Calculates the marginal state occupation probabilities, the state entry and exit time distributions, and the marginal integrated transition hazard for a general, possibly non-Markov, multistate system under left-truncation and right censoring. msSurv also performs nonparametric estimation for state dependent right censored and possibly left truncated data. This package provides functions to find the state occupation probabilities at a specific time and to find the transition probabilities between any two times.
Performs optimal experiment design (OED) for models that cope with large parameter uncertainty. PUA is not limited to any specific error model or assumption regarding the parameter distribution. It enables the modeller to select specific predictions of interest that require decreased uncertainty thereby focus the experimental efforts in order to save time and resources. Furthermore, it allows the prediction of interest to be any quantity that can be obtained from simulations.
A graphical Java Bayesian evidence analysis tool implementing nested sampling - an algorithm yielding an estimate of the log of the Bayesian evidence Z and the moments of model parameters, thus addressing two outstanding challenges in systems modelling. A likelihood function based on the L1-norm is adopted as it is generically applicable to replicated time series data.
Provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler-Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code.
A software package for applying the Bayesian inferential methodology to problems in systems biology. BioBayes provides a framework for Bayesian parameter estimation and evidential model ranking over models of biochemical systems defined using ordinary differential equations. The package is extensible allowing additional modules to be included by developers.
A diffusive transport solver tailored to biological problems. BioFVM can simulate release and uptake of many substrates by cell and bulk sources, diffusion and decay in large 3D domains. It has been parallelized with OpenMP, allowing efficient simulations on desktop workstations or single supercomputer nodes. BioFVM also aims to create a code that makes large-scale transport solvers accessible to desktop computing.
Provides a MATLAB interface for the SUNDIALS solvers CVODES and IDAS. AMICI allows the user to specify differential equation models in terms of symbolic variables in MATLAB and automatically compiles such models as MEX simulation files. In contrast to the SUNDIALSTB interface, all necessary functions are transformed into native C code, which allows for a significantly faster compilation. The interface was designed to provide routines for efficient gradient computation in parameter estimation of biochemical reaction models but is also applicable to a wider range of differential equation constrained optimization problems.
Allows model space exploration for ordinary differential equations (ODE) models based on approximate parameter space exploration. TopoFilter is a method for automatic model generation that needs to evaluate a subset of all possible topologies in the Bayesian framework.
Reads a model of a biochemical reaction network in SBML format and produces a range of diagrams showing different levels of detail. sbml-diff can be used as a python package, allowing it to be incorporated into larger software packages, such as tools for editing and curating collections of models, or incorporated into automated tests to ensure that other tools do not contain bugs that cause unintended changes to SBML files.
Assists users in identification of potential outbreaks of infectious diseases. Netabc is a model-based parametric clustering method that can recover clusters of rapid transmission in simulated data. It is based on a Markov-modulated Poisson process (MMPP) representing the evolution of transmission rates along the tree relating different infections. The program can serve for clustering genetic sequences that have been sampled from an infectious disease epidemic.
Provides a R package introducing a statistical method for modeling disease transmission. The package allows users to settle individual-level spatiotemporal data, in a mechanistic manner or accounting for the general population. The software can be used to simulate epidemics among the general population and subsequently allowing a general epidemic predictive framework.
Uses variational Bayesian inference to learn hidden Markov models from individual, single-molecule fluorescence resonance energy transfer efficiency (EFRET) versus time trajectories. VBFRET identifies states in and infers idealized trajectories from smFRET time series. It implements VBEM for FRET data. This technique can be applied to temporal data such as smFRET time series and shows superior statistical consistency relative to the maximum likelihood approach.
Enables generation of networks with a specified degree distribution, measuring fundamental network characteristics. EpiFire is an application programming interface (API) allowing user to perform these functions in a point-and-click environment and provides intuitive graphical results of epidemic simulations. It includes a continuous time, stochastic mass-action simulation class to permit users to create hybrid models or to compare the results of mass-action and network-based models.
Allows the automated integration of experimental data into theoretical models without requiring programming knowledge from the user. SBMLmod allows data integration and analysis with a minimal number of user required operations. All operations can be performed without further software or programming dependencies. It will contribute to improve data integration into modelling approaches especially with respect to accessibility.
Automates repetitive tasks in model building and simulation. SBpipe builds a sequence of repeated model simulations or parameter estimations, performs analyses from this generated sequence, and finally generates a LaTeX/PDF report. It can execute models implemented in Copasi, Python or coded in any other programming language using Python as a wrapper module. The software aims to encourage the development of pipelines for systems modelling into a single community activity.
Promotes systematic searches within the study design space. LIFESPAN generates a set of alternative models with equal statistical power to detect hypothesized effects, and delineates trade-off relations among relevant parameters, such as total study time and the number of measurement occasions. It boosts the efficiency, breadth, and precision of the search for optimal longitudinal designs. The tool helps researchers to craft and select longitudinal designs that have optimal power to detect random effects of change, based the change process under investigation.