BioNetGMMFit

BioNetGMMFit performs parameter estimation for mechanistic models of signaling and gene regulatory kinetics by integrating BioNetGen and CyGMM and leveraging cell-to-cell variability in multidimensional single-cell and population snapshot datasets.

Key Features:

• Integration of BioNetGen and CyGMM: Combines BioNetGen rule-based model specification (BNGL) with CyGMM-based estimation to analyze biochemical reaction networks.
• Handling of multidimensional single-cell data: Processes time-stamped snapshots of protein and mRNA copy numbers measured across single cells to capture cell-to-cell variability.
• Parameter estimation and model fitting: Estimates model parameters that best fit observed single-cell, time-stamped datasets and provides confidence intervals for estimates.
• Scalability: Fits mechanistic models to datasets spanning varying cell population sizes.

Scientific Applications:

• Systems biology parameter inference: Infers kinetic parameters in biochemical signaling and gene regulatory networks from single-cell data.
• Single-cell kinetics modeling: Supports modeling of signaling dynamics and gene regulation at the single-cell level using time-resolved protein and mRNA measurements.
• Model validation against experimental data: Enables quantitative validation and refinement of mechanistic models using multidimensional single-cell snapshots.

Methodology:

Integrates BioNetGen (BNGL) with CyGMM, uses multidimensional time-stamped single-cell protein and mRNA copy number snapshots to leverage cell-to-cell variability for fitting mechanistic models, and computes parameter estimates with confidence intervals across varying cell population sizes.