McSNAC: A software to approximate first-order signaling networks from mass cytometry data

Area of Science:

• Systems Biology
• Computational Biology
• Cellular Signaling

Background:

• Mass cytometry (CyTOF) enables high-dimensional single-cell protein analysis, offering insights into cellular activity.
• Measuring signaling proteins like phospho-proteins provides dynamic information on single-cell signaling processes.
• Reconstructing complex signaling networks requires advanced computational analysis.

Purpose of the Study:

• To develop and validate a novel software tool, McSNAC, for reconstructing signaling networks from CyTOF data.
• To estimate kinetic parameters of signaling networks.
• To assess the capability of McSNAC in handling complex and partially unmeasured signaling systems.

Main Methods:

• Mass cytometry Signaling Network Analysis Code (McSNAC) software was developed.
• McSNAC models signaling networks as first-order reactions.
• In silico experiments were conducted to evaluate McSNAC's performance.

Main Results:

• McSNAC accurately estimates ground-truth models from first-order systems in a scalable manner.
• McSNAC qualitatively predicts outcomes of perturbations in second-order reaction models.
• McSNAC shows predictive capability in a complex nonlinear signaling network with unmeasured proteins.

Conclusions:

• McSNAC serves as a valuable screening tool for generating signaling network models.
• The software is effective for analyzing time-stamped CyTOF data.
• McSNAC facilitates the mechanistic understanding of cellular signaling pathways.