STSE

STSE performs spatio-temporal simulations within discrete structures using high-resolution microscopy images to model and analyze spatial distributions of biochemical species.

Key Features:

• Spatio-temporal simulation: Simulates temporal dynamics of biochemical species within discrete spatial domains.
• Image-based spatial extraction: Extracts spatial information from high-resolution microscopy images by mapping pixel luminosity to molecular levels.
• Voronoi meshing: Generates meshes of simulation spaces using the Voronoi concept to discretize complex geometries.
• Digitization and representation: Provides modules for digitizing and representing spatial distributions of biochemical species.
• Mathematical modeling and analysis: Implements mathematical modeling and analysis of spatial distributions of biochemical species.
• Python scripting/API: Offers Python scripting interfaces to script and extend analytical and modeling functionalities.
• Cellular morphology analysis: Enables analysis of cellular characteristics including volume, size, geometry, and intracellular compartmentalization.
• Image-driven model validation: Links simulations directly with image data to support systematic model validation or rejection.

Scientific Applications:

• Quantitative morphology: Analysis of volume, size, geometry, and intracellular compartmentalization from microscopy-derived spatial models.
• Hypothesis verification and model selection: Systematic testing and validation or rejection of spatially resolved biochemical models using image data.
• Signaling cascade modeling: Modeling signaling cascades that produce morphological gradients of Fus3 within the cytoplasm of the mating yeast Saccharomyces cerevisiae.

Methodology:

Digitization of microscopy images; Voronoi-based meshing of simulation domains; extraction of pixel luminosity as a proxy for molecular levels; mathematical modeling and analysis of spatial distributions; extensibility via Python scripting.