Computational Modeling and Imaging of the Intracellular Oxygen Gradient

Area of Science:

• Computational biology
• Cellular bioenergetics
• Biophysics

Background:

• Intracellular spatial heterogeneity of solutes like oxygen partial pressure (pO2) is crucial for cellular function.
• Computational modeling offers a mechanistic framework to quantitatively describe these spatial variations.

Purpose of the Study:

• To develop and evaluate a finite-element model for simulating oxygen-consuming mitochondrial bioenergetics.
• To quantitatively predict intracellular and mitochondrial pO2 distributions and their impact on cellular bioenergetics.

Main Methods:

• Developed a finite-element model using COMSOL Multiphysics for oxygen diffusion and consumption kinetics.
• Estimated model parameters from published data for isolated and intact mitochondria.
• Validated the model against experimental pO2 measurements in HeLa cells using FRET-based oxygen sensing.

Main Results:

• The model accurately predicted intracellular and mitochondrial pO2 gradients in HeLa cells across different respiratory states and imposed pO2 levels.
• The model qualitatively predicted integrated experimental data and the impact of altered mitochondrial processes on bioenergetics.
• Demonstrated the utility of Myoglobin-mCherry as a FRET-based sensor for real-time, noninvasive subcellular pO2 measurements.

Conclusions:

• The developed computational model provides a robust framework for understanding intracellular oxygen dynamics and mitochondrial bioenergetics.
• The model can be used to predict cellular responses to varying oxygen levels and perturbations in mitochondrial function.
• Combined computational modeling with advanced biosensing techniques offers powerful insights into cellular physiology.