A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical...

Area of Science:

• Developmental Biology
• Computational Neuroscience
• Systems Biology

Background:

• Understanding gene expression's role in biological patterns is crucial.
• Morphological and dynamical patterns in living organisms are influenced by gene expression.

Purpose of the Study:

• To develop a methodology for quantifying gene expression's influence on cell morphology and dynamics.
• To identify gene expression effects on system dynamics using computational models.

Main Methods:

• Estimating mutual information and Pearson correlation between gene expression and cell morphology.
• Simulating artificial neuronal networks with distinct gene expression patterns.
• Assessing dynamical response using the integrate-and-fire model.

Main Results:

• Gene expression patterns significantly affect the interaction between gene expression and neuronal network topology.
• A strong correlation was observed between gene expression and neuronal network topology for specific patterns.
• Gene expression also influences the dynamics response of neuronal networks.

Conclusions:

• Jointly analyzing neuron morphology, topology, and dynamics is essential for understanding gene expression.
• The proposed framework is a foundational step towards predicting gene expression from morphology and connectivity.