Modelling stem cell differentiation related processes-A practical overview for biologists

Area of Science:

• * Computational biology
• * Developmental biology
• * Regenerative medicine

Background:

• * Stem cell differentiation is crucial for development and regenerative medicine.
• * Experimental control of stem cell differentiation is challenging due to complex underlying processes.
• * Selecting appropriate computational models and data can be difficult for experimental biologists.

Purpose of the Study:

• * To guide experimental biologists in selecting computational models for stem cell differentiation research.
• * To connect different modeling approaches with specific research questions.
• * To outline data requirements for effective model utilization.

Main Methods:

• * Review of various computational modeling types for stem cell differentiation.
• * Discussion of mechanistic dynamic models, including ordinary differential equation (ODE) models.
• * Exploration of abstract, multiscale models and data-driven deep learning approaches.

Main Results:

• * Categorization of computational models based on their representational capacity and insights provided.
• * Explanation of validation strategies and limitations for each model class.
• * Guidance on integrating computational modeling into experimental workflows.

Conclusions:

• * Computational models can aid hypothesis formation and prediction in stem cell differentiation research.
• * Understanding different modeling approaches facilitates their application in experimental settings.
• * This review aims to simplify the incorporation of modeling to accelerate stem cell research and discovery.