A Multiscale Computational Architecture to Study Signaling Dynamics at Cell-Cell Interfaces.

Area of Science:

• Cell Biology
• Systems Biology
• Structural Bioinformatics

Background:

• Intercellular communication relies on dynamic protein complexes at cell-cell interfaces.
• Static models overlook physical constraints like steric hindrance and spatial compartmentalization.
• A gap exists between static network topology and dynamic systems biology.

Purpose of the Study:

• To develop a multi-scale computational framework integrating structural and spatial dynamics of cell-cell interactions.
• To investigate the role of Fibroblast Growth Factor Receptor 1 (FGFR1)-centered signaling motifs in intercellular communication.
• To bridge the gap between static interaction networks and dynamic biological systems.

Main Methods:

• Identified a conserved FGFR1-centered cell adhesion and signaling motif.
• Developed a multi-layer spatial stochastic simulator for dynamic behavior analysis.
• Generated atomic-resolution structural models using AlphaFold to define interaction rules.

Main Results:

• The structural arrangement of adhesion complexes dictates FGFR1 receptor clustering and microdomain formation.
• Decoy receptors capture signaling receptors, regulating downstream processes.
• Altered binding affinity can disrupt receptor organization and signal transduction, potentially leading to disease.

Conclusions:

• Structural interaction rules and spatial constraints are critical for intercellular signaling network formation and function.
• The developed framework integrates interactomics, structural bioinformatics, and stochastic modeling for a multi-scale approach.
• Findings highlight the link between molecular structure, spatial organization, and cellular signaling pathways.