Parameter sensitivity analysis for a stochastic model of mitochondrial apoptosis pathway.

Area of Science:

• * Computational biology
• * Systems biology
• * Cancer research

Background:

• * Understanding oncogenesis, the process of cancer development, is crucial for effective cancer prevention and treatment.
• * Gene alterations are key drivers of oncogenesis, but the precise mechanisms remain an active area of research.
• * Apoptosis, or programmed cell death, is a critical cellular process often dysregulated in cancer.

Purpose of the Study:

• * To investigate the relationship between parameter sensitivity in apoptosis models and oncogenesis.
• * To explore the utility of energy barrier height in stochastic models as an indicator of cancer initiation.
• * To elucidate the logic of cancer initiation using stochastic dynamics and energy landscape concepts.

Main Methods:

• * Performed parameter sensitivity analysis on a mitochondrial apoptosis model.
• * Conducted nonlinear bifurcation analysis for deterministic dynamics.
• * Utilized energy barrier analysis for corresponding stochastic models.

Main Results:

• * Found a high correlation between parameter sensitivity rankings from deterministic (bifurcation points) and stochastic (energy barrier heights) models.
• * Demonstrated that energy barrier height correlates with the transition from a living to a death state in cells.
• * Identified that parameters significantly affecting bifurcation points are linked to frequently mutated oncogenic genes.

Conclusions:

• * Energy barrier height serves as a reliable indicator of oncogenesis, analogous to bifurcation point location.
• * Stochastic dynamics and energy landscape provide a framework for understanding cancer initiation.
• * Established the equivalence of energy barrier height and bifurcation-point location in determining parameter sensitivity for the first time.