Precursors for cell-state transitions: Stability, resilience, and predictability in Notch signaling pathways

Area of Science:

• Cellular dynamics and signaling pathways
• Computational biology and systems modeling
• Biomarker discovery for disease prediction

Background:

• The Notch signaling pathway is vital for development and homeostasis, but its dysregulation is linked to diseases like cancer.
• Identifying reliable markers for Notch signaling transitions is crucial for predicting disease progression and guiding therapies.
• Understanding single-cell level biochemical processes and cellular noise is essential for accurate modeling.

Purpose of the Study:

• To develop a predictive framework for Notch signaling transitions using computational models.
• To investigate the impact of external Jagged levels on cell-state dynamics.
• To evaluate the efficacy of various statistical measures as dynamic biomarkers for cellular plasticity.

Main Methods:

• Developed a deterministic model of the Notch pathway, including receptor, ligands (Delta, Jagged), and NICD.
• Extended the model to a stochastic formulation using the chemical master equation to incorporate cellular noise.
• Employed bifurcation analysis and stochastic simulations, analyzing statistical measures like autocorrelation, variance, and mutual information.

Main Results:

• Variations in external Jagged levels drive cell-state transitions (sender, receiver, hybrid states).
• Statistical measures effectively predict these transitions, though composite measures showed limitations.
• The variability index outperformed Jacobian indices in resilience landscape assessments.
• The hybrid state was identified as the fittest based on basin stability and potential-well evaluations.

Conclusions:

• Cellular transitions within the Notch signaling pathway can be modeled and predicted.
• Statistical tools show promise as dynamic biomarkers for cellular plasticity, with specific metrics outperforming others.
• The study highlights the application and limitations of statistical approaches in understanding complex biological systems and disease states.