ArticleBMC systems biology2013
Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 30 citations in OpenAlex.
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- Parameter Estimation and Uncertainty Quantification for Systems Biology Models.Current opinion in systems biology · 2019Article
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- Predicting network modules of cell cycle regulators using relative protein abundance statistics.BMC systems biology · 2017Article
- Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer.Frontiers in physiology · 2017Article
- A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.PloS one · 2016Article
- Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments.PLoS computational biology · 2016Article
- Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.BMC systems biology · 2015Article
- A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of CD4+ T Cells.Bulletin of mathematical biology · 2015Article
- Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.PLoS computational biology · 2015Article
- Mechanistic modeling confronts the complexity of molecular cell biology.Molecular biology of the cell · 2014Article
- Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step.eLife · 2014Article
- A stochastic model correctly predicts changes in budding yeast cell cycle dynamics upon periodic expression of CLN2.PloS one · 2014Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundParameter estimation from experimental data is critical for mathematical modeling of protein regulatory networks. For realistic networks with dozens of species and reactions, parameter estimation is an especially challenging task. In this study, we present an approach for parameter estimation that is effective in fitting a model of the budding yeast cell cycle (comprising 26 nonlinear ordinary differential equations containing 126 rate constants) to the experimentally observed phenotypes (viable or inviable) of 119 genetic strains carrying mutations of cell cycle genes.
resultsStarting from an initial guess of the parameter values, which correctly captures the phenotypes of only 72 genetic strains, our parameter estimation algorithm quickly improves the success rate of the model to 105-111 of the 119 strains. This success rate is comparable to the best values achieved by a skilled modeler manually choosing parameters over many weeks. The algorithm combines two search and optimization strategies. First, we use Latin hypercube sampling to explore a region surrounding the initial guess. From these samples, we choose ∼20 different sets of parameter values that correctly capture wild type viability. These sets form the starting generation of differential evolution that selects new parameter values that perform better in terms of their success rate in capturing phenotypes. In addition to producing highly successful combinations of parameter values, we analyze the results to determine the parameters that are most critical for matching experimental outcomes and the most competitive strains whose correct outcome with a given parameter vector forces numerous other strains to have incorrect outcomes. These "most critical parameters" and "most competitive strains" provide biological insights into the model. Conversely, the "least critical parameters" and "least competitive strains" suggest ways to reduce the computational complexity of the optimization.
conclusionsOur approach proves to be a useful tool to help systems biologists fit complex dynamical models to large experimental datasets. In the process of fitting the model to the data, the tool identifies suggestive correlations among aspects of the model and the data.
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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.