A neural network-based model framework for cell-fate decisions and development.

Area of Science:

• Developmental Biology
• Systems Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) are crucial for maintaining cell differentiation stability and guiding development.
• Modeling intermediate differentiation stages and their divergent paths presents a significant challenge in developmental biology.

Purpose of the Study:

• To develop an empirical data-based associative GRN model (AGRN) that explains how regulatory networks store gene expression profiles.
• To demonstrate how AGRNs can dynamically drive cell differentiation towards different attractors in response to instructive signals.

Main Methods:

• Developed an associative GRN model (AGRN) based on empirical data.
• Modeled gene regulatory networks storing multilineage stage-specific gene expression profiles as associative memory patterns.
• Simulated responses to multiple instructive signals with varying timing and identity.

Main Results:

• The AGRN model successfully stores gene expression profiles as associative memory.
• The model demonstrates dynamic differentiation of multipotent cells towards distinct cell state attractors based on signal input.
• AGRN dynamics generate diverse lineage-committed cell populations robustly and flexibly.

Conclusions:

• The AGRN model provides an attractor-based explanation for signal-driven cell fate decisions in differentiation.
• This modeling approach offers a generalizable tool for studying various cell specification systems.
• The model addresses the challenge of explaining the stability and divergence of intermediate differentiation stages.