Empirical methods that provide physical descriptions of dynamic cellular processes

Area of Science:

• Cell Biology
• Systems Biology
• Quantitative Biology

Background:

• Dynamic cellular processes are crucial for development and disease.
• Understanding these processes requires robust analytical methods.
• Current methods often focus on spatial data, limiting nonspatial insights.

Purpose of the Study:

• To review empirical methods for nonspatial descriptions of dynamic cellular processes.
• To explore the inference of interaction networks (cell-state lineages, gene regulatory networks, molecular interactions) from observational data.
• To assess the degree to which causal and mathematical relationships can be inferred from observations.

Main Methods:

• Review of analytical perspectives, from statistical associations to causal inference and dynamical systems.
• Focus on data from observations or minimal perturbations, reflecting natural system behavior.
• Development of a "recipe" for empirically determining quantitative relationships and growth laws from live-cell microscopy data.

Main Results:

• Methods are presented in order of increasing descriptive power and required assumptions.
• Discussion covers inferring statistical associations, causal relationships, and precise mathematical models.
• A framework is provided for quantitative biologists to maximize insights from observational data.

Conclusions:

• Observational data, even with minimal perturbation, can yield significant insights into cellular dynamics.
• A spectrum of methods exists, allowing researchers to choose approaches based on system knowledge and desired descriptive power.
• The review encourages the use of diverse quantitative approaches to advance understanding of cellular processes.