Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone

Area of Science:

• Computational biology
• Neuroscience
• Systems biology

Background:

• Cells communicate internal molecular information through external physical signals.
• Cyclic guanosine monophosphate (cGMP) signaling influences membrane potential (MP) shifts, guiding neuronal growth cone direction.
• Understanding these complex signaling pathways is crucial for developmental neuroscience.

Purpose of the Study:

• To develop a novel computational approach for estimating intracellular biomolecular pathways from electrophysiological recordings.
• To integrate a deterministic mathematical model with a Bayesian reverse-engineering framework to analyze cell signaling.
• To identify the most plausible molecular pathway regulating growth cone electrophysiology.

Main Methods:

• Developed an integrated deterministic mathematical model and Bayesian reverse-engineering framework.
• Utilized electrophysiological recordings of growth cone responses as input data.
• Employed a computational method to select the most likely molecular pathway considering system uncertainty and cell variability.

Main Results:

• The model quantitatively reproduces membrane potential shifts based on cGMP levels and variability.
• The computational framework successfully estimates molecular signaling pathways from electrical data.
• The study identified cell-to-cell variability and system uncertainty as key factors in MP regulation.

Conclusions:

• The developed computational method provides a robust way to infer intracellular signaling from cellular electrophysiology.
• The model predicts that cGMP-dependent protein kinase (PKG)-mediated inhibition of chloride channels is essential for MP regulation.
• This approach advances our understanding of molecular mechanisms governing neuronal development and cellular responses.