ArticleThe Journal of biological chemistry2010
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework.
Article in The Journal of biological chemistry, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed, 95 citations in OpenAlex.
- A single mechanism can explain network-wide insulin resistance in adipocytes from obese patients with type 2 diabetes.The Journal of biological chemistry · 2014Trial
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- Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states.PloS one · 2024Article
- Caveolin-1: A Review of Intracellular Functions, Tissue-Specific Roles, and Epithelial Tight Junction Regulation.Biology · 2023Review
- Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.PLoS computational biology · 2023Article
- Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.PLoS computational biology · 2022Article
- Finding new edges: systems approaches to MTOR signaling.Biochemical Society transactions · 2021Review
- Role of Caveolin-1 in Diabetes and Its Complications.Oxidative medicine and cellular longevity · 2020Review
- Insulin and β-adrenergic receptors mediate lipolytic and anti-lipolytic signalling that is not altered by type 2 diabetes in human adipocytes.The Biochemical journal · 2019Article
- Article
- Benchmark problems for dynamic modeling of intracellular processes.Bioinformatics (Oxford, England) · 2019Article
- Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates.Bioinformatics (Oxford, England) · 2018Article
- A systems biology analysis connects insulin receptor signaling with glucose transporter translocation in rat adipocytes.The Journal of biological chemistry · 2017Article
- Understanding the mTOR signaling pathway via mathematical modeling.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients.Bioscience reports · 2017Article
- Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes.The Journal of biological chemistry · 2016Article
- Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI.PLoS computational biology · 2016Article
- A rule-based model of insulin signalling pathway.BMC systems biology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.
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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.