iNA is a computational tool for quantitative analysis of fluctuations in biochemical reaction networks. Such fluctuations, also known as intrinsic noise, arise due to the stochastic nature of chemical reactions and cannot be ignored for when some molecules are present only in very low copy numbers as is the case in living cells. The SBML-based software computes statistical measures such as means and standard deviations of concentrations within a given accuracy using the analytical system size expansion. The result of iNA’s analysis can be tested against the computationally much more expensive stochastic simulation algorithm.
The Graphical Models Toolkit (GMTK) is a toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). It can be used for speech and language processing, bioinformatics, activity recognition, and any time series application. It features exact and approximate inference, many built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors, arbitrary order embedded Markov chains, a GUI graph viewer, and much more.