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.
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.
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.
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.
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 access to all Schrödinger's computational technology. Maestro 11 tries to help researchers in the organization and the analysis of data. This tool uses a modern, intuitive environment with significantly enhanced usability.
Generates and discriminates model alternatives. modelIMaGe generates SBML or Copasi candidate models by removing specified model components from a given master model and automatically documents them. The generated models are portable to any other SBML compliant software, which gives to user the possibility to view and analyze them with an array of already existing tools.
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.