A robust structural PGN model for control of cell-cycle progression stabilized by negative feedbacks.

Area of Science:

• Systems Biology
• Computational Biology
• Genetics

Background:

• The cell division cycle is a fundamental biological process regulated by a complex genetic network.
• Existing models often struggle to capture the inherent robustness of biological systems against perturbations.
• Understanding the dynamics of genetic regulatory networks is crucial for deciphering cellular processes.

Purpose of the Study:

• To develop a robust hypothetical model of the cell division cycle using a probabilistic genetic network (PGN).
• To investigate the dynamical behavior and resilience of the PGN model under various noise conditions.
• To compare the performance of the PGN model against existing deterministic models.

Main Methods:

• Construction of a probabilistic genetic network (PGN) model inspired by known biological mechanisms like feedback loops and redundant pathways.
• Representation of gene interactions as stochastic processes to simulate noise.
• Analysis of model dynamics and robustness against parameter fluctuations and environmental noise.

Main Results:

• The PGN model exhibits significant robustness to moderate noise and parameter fluctuations, characteristic of biological cell cycles.
• The PGN model outperforms a recently published deterministic yeast cell-cycle model under similar noise conditions.
• Inclusion of self-stimulatory mechanisms allows the PGN model to exhibit pacemaker activity, mimicking embryonic cell cycles.

Conclusions:

• Probabilistic genetic network models offer a powerful framework for capturing the robustness of biological systems like the cell division cycle.
• The developed PGN model provides a more accurate representation of cell cycle dynamics under noisy conditions than deterministic approaches.
• The PGN model's ability to simulate pacemaker activity opens avenues for studying oscillatory biological rhythms.