Measuring Regulatory Network Inheritance in Dividing Yeast Cells Using Ordinary Differential Equations

Area of Science:

• Cell biology
• Systems biology
• Quantitative biology

Background:

• Inheritance of protein regulation during asymmetric cell division is complex and lacks formal mathematical definition.
• Understanding this inheritance is crucial for predicting cell fate and responses to stress.

Purpose of the Study:

• To introduce ODEinherit, a novel statistical framework for quantifying protein regulatory network inheritance during cell division.
• To address the challenge of measuring how much of a mother cell's regulatory network is passed to its daughters.

Main Methods:

• Utilizing ordinary differential equations (ODEs) to estimate cell-specific regulatory networks.
• Incorporating adjustments for non-oscillatory trajectories and network density.
• Quantifying inheritance by assessing a mother's network's explanatory power for daughter cell trajectories.

Main Results:

• ODEinherit accurately quantifies inheritance, with precise measurement depending on network density adjustments.
• Benchmarked on simulated data and applied to live-cell microscopy of 85 dividing S. cerevisiae cells.
• Revealed substantial heterogeneity in inheritance rates among mother-daughter cell pairs.

Conclusions:

• ODEinherit provides a robust method for quantifying protein regulatory inheritance.
• The observed heterogeneity has implications for understanding cellular stress response and cell-fate prediction.
• This framework opens new avenues for studying inheritance across cell generations.