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BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells
John Wu1,2, William C L Stewart3, Ciriyam Jayaprakash4
1Department of Computer Science, The Ohio State University, 281 W Lane Ave, Columbus, OH, 43210, USA. wu.4427@osu.edu.
NPJ Systems Biology and Applications
|September 22, 2023
Summary
BioNetGMMFit integrates BioNetGen and CyGMM to accurately estimate parameters for mechanistic models using single-cell, time-stamped data. This tool enhances systems biology research by enabling precise quantitative predictions of cellular kinetics.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemistry
Background:
- Mechanistic models are crucial for understanding cellular signaling and gene regulation kinetics.
- Single-cell technologies generate multidimensional, time-stamped data of molecular abundances.
- Accurate parameter estimation is essential for validating models and making quantitative predictions.
Purpose of the Study:
- To develop a software package that facilitates accurate parameter estimation from multidimensional single-cell data.
- To integrate rule-based modeling (BioNetGen) with cell-to-cell variation analysis (CyGMM).
- To provide a user-friendly framework for systems biology researchers.
Main Methods:
- Merged BioNetGen and CyGMM into a single software package: BioNetGMMFit.
- Utilized BioNetGen markup language (BNGL) for defining mechanistic models.
- Employed cell-to-cell differences to improve parameter estimation accuracy.
- Generated confidence intervals for model parameters.
Main Results:
- BioNetGMMFit provides accurate parameter estimates for mechanistic models based on single-cell data.
- The software can fit datasets of increasing cell population sizes.
- Confidence intervals enhance the precision of parameter estimates.
Conclusions:
- BioNetGMMFit streamlines the development and fitting of mechanistic models for large single-cell datasets.
- The software offers an accessible, scalable modeling framework for the biochemical signaling community.
- Enables quantitative predictions of signaling and gene regulatory kinetics.
