ArticlePLoS computational biology2016
The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- EMBED: Ensemble MATCONT-Based Detection and Classification of Bifurcations in Dual Phosphorylation-Dephosphorylation Reaction Networks.ACS omega · 2026Article
- A cellular automaton model of osteogenic differentiation reveals identifiability limits of endpoint assays.Frontiers in systems biology · 2026Article
- Development of a mathematical model for microbial consumption of glucose and fructose during cocoa (Microbiology spectrum · 2025Article
- The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective.PloS one · 2025Article
- Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers.Canadian prosthetics & orthotics journal · 2025Review
- Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Association · 2024Review
- Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.ACS synthetic biology · 2024Review
- Wound management, healing, and early prosthetic rehabilitation: Part 2 - A scoping review of physical biomarkers.Canadian prosthetics & orthotics journal · 2024Review
- Sloppiness: Fundamental study, new formalism and its application in model assessment.PloS one · 2023Article
- Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.Science advances · 2022Article
- Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis.BMC bioinformatics · 2021Article
- Parameter estimation in fluorescence recovery after photobleaching: quantitative analysis of protein binding reactions and diffusion.Journal of mathematical biology · 2021Article
- Cancer Explant Models.Current topics in microbiology and immunology · 2021Article
- An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infectionsJournal of the Royal Society, Interface · 2020Article
- Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data.Frontiers in physiology · 2020Article
- Investigating the Physical Effects in Bacterial Therapies for Avascular Tumors.Frontiers in microbiology · 2020Article
- Article
- Parameter estimation and identifiability in a neural population model for electro-cortical activity.PLoS computational biology · 2019Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- Fisher information and Cramér-Rao lower bound for experimental design in parallel imaging.Magnetic resonance in medicine · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model's discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.
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Registered trials
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.