PyFRAP

PyFRAP performs numerical simulation and model fitting of three-dimensional FRAP and inverse FRAP (iFRAP) experiments to quantify molecular diffusivities and reaction kinetics in systems with non-uniform bleaching/photoconversion and complex geometries.

Key Features:

• Three-dimensional numerical simulation and fitting: Numerical simulations of three-dimensional models are fitted to experimental FRAP and iFRAP data.
• Inhomogeneous bleaching and photoconversion: Accounts for spatial inhomogeneities in bleaching and photoconversion processes during experiments.
• Geometry and reaction–diffusion modeling: Models sample geometry and reaction–diffusion kinetics rather than assuming uniform conditions.
• Diffusivity quantification across molecular weights: Determines diffusivities of fluorescent molecules across a broad range spanning two orders of magnitude in molecular weight.
• Cell-like obstacles and tortuosity: Quantifies the impact of cell-like obstacles on effective diffusivity and tortuous transport paths.
• Model selection for reaction kinetics: Incorporates model selection techniques to account for and distinguish reaction kinetics.

Scientific Applications:

• Quantification of molecular mobility: Deriving molecular mobility and diffusivity parameters from FRAP and iFRAP assays.
• Impact of spatial heterogeneity: Assessing how cell-like obstacles and spatial heterogeneities alter effective diffusivity and tortuosity.
• Reaction versus diffusion analysis: Inferring reaction kinetics and distinguishing diffusion-limited versus reaction-limited behaviors using model selection.
• Comparative diffusivity studies: Comparing diffusivities across molecules spanning two orders of magnitude in molecular weight.

Methodology:

Numerical simulations of three-dimensional models are fitted to experimental FRAP/iFRAP data, explicitly handling inhomogeneous bleaching/photoconversion and employing model selection to account for reaction kinetics.