A bifurcating autoregression model for cell lineages with variable generation means

Area of Science:

• Quantitative Biology
• Biomathematics
• Cell Biology

Background:

• Cell lineage tracing is crucial for understanding cell development and population dynamics.
• Existing autoregression models may not fully capture variations in cell characteristics across generations.
• Accurate modeling is needed to distinguish inherited traits from environmental influences.

Purpose of the Study:

• To extend the bifurcating autoregression model for cell lineage data.
• To incorporate observations with means that vary across generations.
• To provide a robust framework for analyzing cell population heterogeneity.

Main Methods:

• Developed an extended bifurcating autoregression model.
• Employed maximum likelihood estimation to derive model parameters.
• Applied the model to estimate generation means and overall variance.
• Utilized correlation analyses to assess inherited and environmental effects.

Main Results:

• The extended model successfully accommodates generational mean variations in cell lineage data.
• Maximum likelihood estimates provided accurate estimations of generation means and variance.
• The model effectively quantified mother-daughter correlations (inherited effects) and sister-sister correlations (environmental effects).

Conclusions:

• The enhanced bifurcating autoregression model offers a powerful tool for analyzing complex cell lineage data.
• This approach allows for a more nuanced understanding of factors driving cell population growth and differentiation.
• The model's application to EMT6 cells and E. coli demonstrates its broad utility in biological research.