ArticleBioinformatics (Oxford, England)2014
Topological augmentation to infer hidden processes in biological systems.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 12 citations in OpenAlex.
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.Journal of the Royal Society, Interface · 2017Article
- Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.PLoS computational biology · 2016Article
- Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection.PLoS computational biology · 2016Article
- Learning (from) the errors of a systems biology model.Scientific reports · 2016Article
- Topological sensitivity analysis for systems biology.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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Abstract
motivationA common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data.
resultsTopological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. AVAILABILITY AND IMPLEMENTATION: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index
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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.