Modeling Collective Cell Migration in a Data-Rich Age: Challenges and Opportunities for Data-Driven Modeling.

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Mathematical modeling is crucial for understanding collective cell migration in development, disease, and regenerative medicine.
• Traditional models often use partial differential equations for cell density and signaling, relying on simplified phenomenological descriptions.
• Emerging experimental technologies generate quantitative data, enabling new modeling paradigms.

Purpose of the Study:

• To provide an overview of data-driven modeling approaches for collective cell migration.
• To outline methodologies for leveraging statistical and machine learning tools.
• To discuss challenges in applying these data-driven methods to real-world biological data.

Main Methods:

• Review of recently developed data-driven modeling techniques.
• Focus on statistical and machine learning tools for model inference.
• Integration of quantitative experimental data for model development.

Main Results:

• Data-driven approaches offer a powerful alternative to traditional modeling.
• Machine learning can determine mathematical models directly from experimental data.
• These methods allow for more accurate and mechanistic insights into cell migration.

Conclusions:

• The advent of quantitative experimental data necessitates advanced modeling strategies.
• Data-driven modeling, particularly using machine learning, is transforming the study of collective cell migration.
• Future research should focus on refining these methodologies and addressing associated challenges for deeper biological understanding.