cellssm

cellssm applies state-space models to statistically analyze time-series organelle movement data, enabling quantitative inference of directional transport dynamics such as chloroplast responses to light in eukaryotic cells.

Key Features:

• State-Space Modeling: Implements state-space models to estimate transition states and quantify directional movements from time-series organelle position data.
• R Implementation: Provided as an R package for performing the computational analyses described.
• Experimental Validation: Demonstrated accurate estimation of movement start times and detection of acceleration during transport to irradiated areas in chloroplast experiments on Marchantia polymorpha.
• Estimation of Common Dynamics: Includes methods to estimate common dynamics among multiple chloroplasts within individual cells to assess cellular variability.
• Intracellular Signal Inference: Can infer variation in intracellular signal transfer speeds associated with organelle transport.
• General Applicability: Applicable to analysis of directional movements including accumulation and avoidance behaviors at cellular and subcellular levels.

Scientific Applications:

• Chloroplast Movement Analysis: Quantitative study of chloroplast relocation in response to light stimuli, including experiments in Marchantia polymorpha.
• Directional Organelle Dynamics: Analysis of accumulation and avoidance movements of organelles at cellular and subcellular scales.
• Intracellular Signal Dynamics: Investigation of signal transfer speeds and acceleration during organelle transport within cells.

Methodology:

Capture time-series organelle movement data and apply state-space models to estimate transition states and common dynamics among multiple organelles, enabling statistical interpretation of directional transport.