Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate...

Area of Science:

• Mathematical Biology
• Computational Biology
• Statistical Inference

Background:

• Deterministic models of cell invasion often face parameter identifiability challenges.
• Previous inference methods for cell invasion models were limited by the type of data used.

Purpose of the Study:

• To develop a parameter estimation method for a stochastic cell invasion model.
• To address parameter identifiability issues present in deterministic models.
• To leverage richer experimental data, including cell trajectories.

Main Methods:

• Utilized approximate Bayesian computation (ABC) for parameter estimation.
• Employed an efficient sequential Monte Carlo (SMC) based ABC algorithm.
• Incorporated fluorescent cell cycle labelling data, including proliferation, migration, and crowding effects.

Main Results:

• The stochastic model, combined with ABC, successfully overcomes parameter identifiability problems.
• Cell trajectory data provides significantly more information for parameter estimation than cell density data.
• The developed method effectively harnesses features from cell count and trajectory data.

Conclusions:

• Stochastic modeling and advanced inference techniques like ABC are crucial for accurate cell invasion modeling.
• Cell trajectory data is highly valuable for resolving parameter ambiguity in biological models.
• The study provides open-source implementations for the simulation model and ABC algorithm.