Deterministic and stochastic approaches to a minimal model for the transition from autophagy to apoptosis

Area of Science:

• Cellular biology
• Systems biology
• Biophysics

Background:

• Autophagy and apoptosis are key cellular processes with interconnected regulatory mechanisms.
• The precise molecular interplay and transitions between autophagy and apoptosis remain incompletely understood.
• Autophagy's cytoprotective role involves inhibiting apoptosis, while apoptosis can suppress autophagy.

Purpose of the Study:

• To develop a minimal model for exploring the transition dynamics between autophagy and apoptosis.
• To integrate deterministic and stochastic cellular dynamics for a comprehensive analysis.
• To investigate the role of noise in regulating the autophagy-apoptosis switch.

Main Methods:

• Developed a minimal mathematical model integrating deterministic and stochastic cellular dynamics.
• Analyzed system bistability and statistical properties under varying stress levels and noise strengths.
• Investigated the impact of noise on feedback loops and cell fate transitions.

Main Results:

• The cellular system demonstrated bistability, indicating distinct states of autophagy and apoptosis.
• Noise was found to significantly affect the double negative feedback loops between autophagy and apoptosis.
• Increased noise strength led to more random transitions between cellular states, particularly under continuous stress variations.

Conclusions:

• The study provides a novel computational framework for understanding autophagy-apoptosis crosstalk.
• Noise plays a critical role in modulating cell fate decisions and transition dynamics.
• Findings offer potential avenues for future experimental validation and therapeutic targeting.