Dynamic modelling of cell cycle arrest through integrated single-cell and mathematical modelling approaches

Area of Science:

• Cellular and Molecular Biology
• Systems Biology
• Computational Biology

Background:

• Highly multiplexed imaging provides static snapshots of cell states.
• Pseudo-time analysis converts static data into dynamic cell trajectories.
• Integrating multiple experimental conditions into mathematical models is challenging.

Purpose of the Study:

• To develop methods for integrating multiplexed, multi-condition immunofluorescence data with mathematical modeling.
• To enable dynamic modeling of cell populations with both oscillatory and arrested states.
• To create a framework applicable to various high-content biological measurement techniques.

Main Methods:

• Proposed data processing and model training strategies for multiplexed immunofluorescence data.
• Developed training strategies for mathematical models accommodating oscillatory and arrested cell dynamics.
• Applied methods to train a cell cycle model using MCF-10A mammary epithelial cell data.

Main Results:

• Successfully trained a cell cycle model on multi-condition immunofluorescence data.
• Validated the model by predicting growth factor sensitivities and responses to inhibitors.
• Demonstrated the framework's applicability to cells with varying initial conditions and dynamics.

Conclusions:

• The developed framework facilitates dynamic modeling of complex biological systems using multiplexed data.
• This approach enhances the predictive power of mathematical models derived from static cell snapshots.
• The framework is generalizable to other high-content measurement techniques, broadening access to dynamic modeling.