A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy

Marchetti, Luca; Reali, Federico; Dauriz, Marco; Brangani, Corinna; Boselli, Linda; Ceradini, G.; Bonora, Enzo; Bonadonna, Riccardo C.; Priami, Corrado

doi:10.1038/srep36029

Cited by 12 publications

(8 citation statements)

References 57 publications

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“…It has been reported that the onset of most type 1 diabetes is related to insulin production deficiency and injury of islet tissues (40,41). Type 1 diabetes results from a chronic autoimmune process continuing for years after presentation (42).…”

Section: Hbxip Participates In the Transcription Of Insulinmentioning

confidence: 99%

Hepatitis B X-interacting protein promotes the formation of the insulin gene–transcribing protein complex Pdx-1/Neurod1 in animal pancreatic β-cells

Wang

et al. 2018

Journal of Biological Chemistry

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The activation of insulin gene transcription depends on multiple nuclear proteins, including the transcription factors PDX-1 and NEUROD1, which form a transcriptional complex. We recently reported that hepatitis B X-interacting protein (HBXIP, also termed LAMTOR5) can modulate glucose metabolism reprogramming in cancer cells. However, the physiological role of HBXIP in the modulation of glucose metabolism in normal tissues is poorly understood. Here, we report that Hbxip is an essential regulator of the effect of the Pdx-1/Neurod1 complex on insulin gene transcription in murine pancreatic β-cells and We found that pancreatic β-cell-specific Hbxip-knockout mice displayed higher fasting blood glucose levels and impaired glucose tolerance. Furthermore, Hbxip was involved in the regulation of insulin in the pancreas islets and increased insulin gene expression in rat pancreatic β-cells. Mechanistically, Hbxip stimulated insulin enhancer activity by interacting with Pdx-1 and recruiting Neurod1 to Pdx-1. Functionally, we provide evidence that Hbxip is required for Pdx-1/Neurod1-mediated insulin expression in rat pancreatic β-cells. Collectively, these results indicate that Hbxip is involved in the transcription of insulin by increasing the levels of the Pdx-1/Neurod1 complex in animal pancreatic β-cells. Our finding provides the insight into the mechanism by which Hbxip stimulates the transcription of the insulin gene.

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Section: Hbxip Participates In the Transcription Of Insulinmentioning

confidence: 99%

Hepatitis B X-interacting protein promotes the formation of the insulin gene–transcribing protein complex Pdx-1/Neurod1 in animal pancreatic β-cells

Wang

et al. 2018

Journal of Biological Chemistry

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“…This problem is often formulated in the form of least squares, as in models related to diabetes [10,19,20], biological pathways [21][22][23][24], and pharmacokinetics/pharmacodynamics [25,26]. Least squares problems may arise in statistical regression as well [27][28][29][30][31][32].…”

Section: Least Squares Methodsmentioning

confidence: 99%

“…For instance, the parameters may represent biological rates or physical quantities that cannot be negative or that are admissible only inside a specific interval. In other cases, the value of certain parameters may depend on other parameters, as in the case when their sum should be smaller or equal to a certain threshold [10]. We refer to the space where the parameters θ can vary, according to constraints, as the space of parameters.…”

Section: Model Tuning and Biomarker Identification As Global Optimizamentioning

confidence: 99%

Optimization Algorithms for Computational Systems Biology

Reali

Priami

Marchetti

2017

Front. Appl. Math. Stat.

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Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology.

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“…In this context, a number of mathematical models of glucose and insulin dynamics have been developed to allow the description and interpretation of such processes, which are often not accessible to direct measurement in vivo [ 5 ]. These models usually apply ordinary differential equations (ODEs) or delay differential equations (DDEs) to describe the physiology of the glucose-insulin system in different experimental conditions and with varying degrees of detail [ 6 – 14 ]. Originally built upon the parsimony principle in order to soften the complexity of experimental protocols and computational efforts, the first modeling milestone is represented by the so called “minimal model” [ 8 ], which was originally applied to estimate insulin sensitivity by inspecting the time courses of insulin and glucose after an intravenous glucose tolerance test (IVGTT).…”

Section: Introductionmentioning

confidence: 99%

A closed-loop multi-level model of glucose homeostasis

et al. 2018

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BackgroundThe pathophysiologic processes underlying the regulation of glucose homeostasis are considerably complex at both cellular and systemic level. A comprehensive and structured specification for the several layers of abstraction of glucose metabolism is often elusive, an issue currently solvable with the hierarchical description provided by multi-level models. In this study we propose a multi-level closed-loop model of whole-body glucose homeostasis, coupled with the molecular specifications of the insulin signaling cascade in adipocytes, under the experimental conditions of normal glucose regulation and type 2 diabetes.Methodology/Principal findingsThe ordinary differential equations of the model, describing the dynamics of glucose and key regulatory hormones and their reciprocal interactions among gut, liver, muscle and adipose tissue, were designed for being embedded in a modular, hierarchical structure. The closed-loop model structure allowed self-sustained simulations to represent an ideal in silico subject that adjusts its own metabolism to the fasting and feeding states, depending on the hormonal context and invariant to circadian fluctuations. The cellular level of the model provided a seamless dynamic description of the molecular mechanisms downstream the insulin receptor in the adipocytes by accounting for variations in the surrounding metabolic context.Conclusions/SignificanceThe combination of a multi-level and closed-loop modeling approach provided a fair dynamic description of the core determinants of glucose homeostasis at both cellular and systemic scales. This model architecture is intrinsically open to incorporate supplementary layers of specifications describing further individual components influencing glucose metabolism.

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A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy

Cited by 12 publications

References 57 publications

Hepatitis B X-interacting protein promotes the formation of the insulin gene–transcribing protein complex Pdx-1/Neurod1 in animal pancreatic β-cells

Hepatitis B X-interacting protein promotes the formation of the insulin gene–transcribing protein complex Pdx-1/Neurod1 in animal pancreatic β-cells

Optimization Algorithms for Computational Systems Biology

A closed-loop multi-level model of glucose homeostasis

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