Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches.

Area of Science:

• Cellular and Molecular Biology
• Computational Biology
• Biophysics

Background:

• Highly multiplexed imaging assays provide static snapshots of cell states.
• Pseudo-time techniques enable dynamic trajectory analysis from static data.
• Integrating multiple experimental conditions into models remains a challenge.

Purpose of the Study:

• To develop data processing and model training approaches for integrating multiplexed, multi-condition immunofluorescence data with mathematical modeling.
• To enable dynamic modeling of cellular processes, such as the cell cycle, across diverse experimental conditions.
• To overcome technical limitations hindering the predictive power of current models.

Main Methods:

• Proposed novel data processing and model training strategies.
• Developed training approaches for oscillatory and arrested cellular dynamics.
• Applied methods to a cell cycle model using MCF-10A mammary epithelial cell data.
• Integrated multiplexed, multi-condition immunofluorescence data.

Main Results:

• Successfully trained a cell cycle model on complex, multi-condition data.
• Validated the model by predicting growth factor sensitivities and inhibitor responses.
• Demonstrated applicability to datasets with both oscillatory and arrested dynamics.
• Framework integrates multiplexed immunofluorescence data for mathematical modeling.

Conclusions:

• The proposed framework facilitates dynamic modeling of cellular processes using multiplexed imaging data.
• This approach enhances the predictive power of mathematical models by integrating multiple conditions.
• The framework is expected to generalize to other high-content measurement techniques like mass cytometry.
• Enables deeper biological insights through accessible dynamic modeling of large datasets.