ArticleProceedings of the National Academy of Sciences of the United States of America2014
Topological sensitivity analysis for systems biology.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 102 citations in OpenAlex.
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- Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.ACS synthetic biology · 2022Article
- Optimal control problem arising from COVID-19 transmission model with rapid-test.Results in physics · 2022Article
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- Turing pattern design principles and their robustness.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Review
- A Bayesian semi-parametric model for thermal proteome profiling.Communications biology · 2021Article
- Identifying the sources of structural sensitivity in partially specified biological models.Scientific reports · 2020Article
- Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.Journal of the Royal Society, Interface · 2020Article
- A quantitative and qualitative analysis of the COVID-19 pandemic model.Chaos, solitons, and fractals · 2020Article
- Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions.PLoS computational biology · 2020Article
- Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.Cell systems · 2020Article
- Article
- Parametric and non-parametric gradient matching for network inference: a comparison.BMC bioinformatics · 2019Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.
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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.