ArticleNature communications2018
Using both qualitative and quantitative data in parameter identification for systems biology models.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.Frontiers in immunology · 2026Article
- Article
- CrossLabFit: A novel framework for integrating qualitative and quantitative data across multiple labs for model calibration.PLoS computational biology · 2025Article
- Model certainty in cellular network-driven processes with missing data.PLoS computational biology · 2023Article
- Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae.FEMS yeast research · 2022Review
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
- Modeling the Nonlinear Dynamics of Intracellular Signaling Networks.Bio-protocol · 2021Article
- Unsupervised logic-based mechanism inference for network-driven biological processes.PLoS computational biology · 2021Article
- Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry.Molecular biology of the cell · 2021Article
- PEtab-Interoperable specification of parameter estimation problems in systems biology.PLoS computational biology · 2021Article
- Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.Journal of mathematical biology · 2020Article
- Bayesian inference using qualitative observations of underlying continuous variables.Bioinformatics (Oxford, England) · 2020Article
- Parameter Estimation and Uncertainty Quantification for Systems Biology Models.Current opinion in systems biology · 2019Article
- Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution.Current opinion in systems biology · 2019Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.