A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Biological system modeling accelerates discovery but faces identifiability issues due to limited data.
• Network models and parameters are often not uniquely constrained by noisy, coarse experimental data.

Purpose of the Study:

• To evaluate a simplified yeast cell-cycle network model's ability to replicate observed transcriptomic behaviors under genomic mutations.
• To assess the impact of matching multiple datasets on the identifiability issue in biological network modeling.

Main Methods:

• Utilized an asynchronous multi-level Boolean approach to model the yeast cell-cycle network.
• Matched time-series transcriptomic data from both cycling and checkpoint-arrested yeast cells to model predictions.

Main Results:

• The simplified network model successfully reproduced dynamical behaviors similar to experimental datasets in most cases.
• Matching multiple datasets significantly reduced the severity of the identifiability problem for the network model.

Conclusions:

• A simplified network model can capture complex biological dynamics and improve predictive accuracy.
• Integrating multiple datasets is a viable strategy to overcome identifiability challenges in systems biology.