APBS is a software package for the numerical solution of the Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions between molecular solutes in salty, aqueous media. Continuum electrostatics plays an important role in several areas of biomolecular simulation, including simulation of diffusional processes to determine ligand-protein and protein-protein binding kinetics, implicit solvent molecular dynamics of biomolecules, solvation and binding energy calculations to determine ligand-protein and protein-protein equilibrium binding constants and aid in rational drug design, and biomolecular titration studies.
Tol is a programming language dedicated to the world of statistics and focused on time series analysis and stochastic processes. Unlike many other languages, Tol is not an object-oriented language, but a declarative language driven by functions, based on two key features: simple syntactical rules and a powerful set of extensible data types.
gfit analyzes data using models. gfit lets the user create a model for virtually any type of system using a minimal amount of computer code. It is particularly useful for studying various systems in biophysics, biochemistry, and cell biology. The interface for gfit models specifies relationships between input and output variables in a rule-based fashion. It provides flexibility and allows the user to reuse same models for many related problems.
PED is a dialogue management system that uses a probabilistic nested belief model to choose dialogue strategies. The dialogue system designer need only supply a set of plan rules to PED as a dialogue grammar with preconditions. Using this grammar, PED constructs game trees (like the one below) to represent the outcomes of the dialogue, so that a dialogue strategy can be chosen for the current turn in the dialogue. PED automatically maintains a belief model by a belief revision process that uses the observed acts in the dialogue. The game tree is evaluated in the context of this belief model. PED is efficient because it uses probabilistic estimates of belief rather than a plain logical belief model.