Reaction-diffusion modelling for microphysiometry on cellular specimens

Area of Science:

• Biotechnology
• Chemical Engineering
• Biophysics

Background:

• Real-time monitoring of cellular reactions in microscale volumes is essential for biological research.
• Integrated sensors for pH and dissolved oxygen are commonly used but their accuracy can be affected by complex factors.
• Understanding the influence of spatiotemporal dynamics on sensor data is critical for reliable results.

Purpose of the Study:

• To quantify the impact of spatiotemporal dynamics on sensor data accuracy in microscale reaction volumes.
• To model cellular reactions, including proton extrusion and oxygen consumption, in complex buffering solutions.
• To analyze the effect of buffering species on proton diffusion and sensor response time.

Main Methods:

• A 3D finite element model was developed to simulate diffusion and metabolic reactions.
• The model incorporated cellular metabolism (proton extrusion, oxygen consumption) and buffering effects.
• Sensor impulse response time was implemented using linear convolution to mimic real-world sensor delays.
• Model validation was performed using an electrochemical approach.

Main Results:

• Significant deviations were observed between measured and actual pH and dissolved oxygen values within the cell culture volume.
• The study detailed the effect of buffering species on proton diffusion dynamics.
• The model demonstrated that sensor placement and spatiotemporal factors influence data interpretation.

Conclusions:

• Spatiotemporal dynamics introduce significant errors in sensor readings from microscale cell cultures.
• Accurate interpretation of real-time monitoring data requires accounting for diffusion, reaction, and buffering effects.
• The developed model is applicable to various biosensor applications involving dissolved gases and ion diffusion in buffered solutions.