Modeling SMS driven conversion of ceramide to sphingomyelin reveals the existence of a positive feedback mechanism

In questa tesi presentiamo un modello matematico minimo per la conversione di un ceramide in sfingomielina catalizzata dall'enzima sfingomielina sintasi 1 (SMS1) basato sulle leggi della cinetica chimica. Viene dimostrato, utilizzando tecniche di sampling per la stima parametrica e metodi di analisi matematica, che questo modello non è in grado di riprodurre qualitativamente delle misure sperimentali sulla composizioni dei lipidi in seguito ad alterazione dell'attivita enzimatica di SMS1. Concludiamo quindi che è necessario considerare un meccanismo di feedback positivo fra i prodotti e i reagenti della reazione, che esiste effettivamente in vivo tramite la proteina chinasi D e la proteina di trasporto di ceramide CERT. Di conseguenza, proponiamo un secondo modello modificato in modo da comprendere questo meccanismo di feedback, che risulta essere in grado di spiegare i risultati sperimentali // Here we present a minimal mathematical model for the Sphingomyelin synthase 1 (SMS1) driven conversion of ceramide to sphingomyelin based on chemical reaction kinetics. We demonstrate, via sampling-based parameter estimation and mathematical analysis, that this model is not able to qualitatively reproduce experimental measurements on lipid compositions after altering SMS1 activities. We conclude that a positive feedback mechanism is required from the products to the reactants of the reaction, which in fact exists in vivo via protein kinase D and the ceramide transfer protein CERT. Accordingly, a modified model that comprises this feedback mechanism was able to reproduce experimental findingsope

The Union between Structural and Practical Identifiability Makes Strength in Reducing Oncological Model Complexity: A Case Study

Mathematical models are increasingly proposed to describe tumor's dynamic response to treatments with the aims of improving their efficacy. The most widely used are nonlinear ODE models, whose identification is often difficult due to experimental limitations. We focus on the issue of parameter estimation in model-based oncological studies. Given their complexity, many of these models are unidentifiable having an infinite number of parameter solutions. These equivalently describe experimental data but are associated with different dynamic evolution of unmeasurable variables. We propose a joint use of two different identifiability methodologies, structural identifiability and practical identifiability, which are traditionally regarded as disjoint. This new methodology provides the number of parameter solutions, the analytic relations between the unidentifiable parameters useful to reduce model complexity, a ranking between parameters revealing the most reliable estimates, and a way to disentangle the various causes of nonidentifiability. It is implementable by using available differential algebra software and statistical packages. This methodology can constitute a powerful tool for the oncologist to discover the behavior of inaccessible variables of clinical interest and to correctly address the experimental design. A complex model to study "in vivo" antitumor activity of interleukin-21 on tumor eradication in different cancers in mice is illustrated

The Union between Structural and Practical Identifiability Makes Strength in Reducing Oncological Model Complexity: A Case Study

Mathematical models are increasingly proposed to describe tumor’s dynamic response to treatments with the aims of improving their efficacy. The most widely used are nonlinear ODE models, whose identification is often difficult due to experimental limitations. We focus on the issue of parameter estimation in model-based oncological studies. Given their complexity, many of these models are unidentifiable having an infinite number of parameter solutions. These equivalently describe experimental data but are associated with different dynamic evolution of unmeasurable variables. We propose a joint use of two different identifiability methodologies, structural identifiability and practical identifiability, which are traditionally regarded as disjoint. This new methodology provides the number of parameter solutions, the analytic relations between the unidentifiable parameters useful to reduce model complexity, a ranking between parameters revealing the most reliable estimates, and a way to disentangle the various causes of nonidentifiability. It is implementable by using available differential algebra software and statistical packages. This methodology can constitute a powerful tool for the oncologist to discover the behavior of inaccessible variables of clinical interest and to correctly address the experimental design. A complex model to study “in vivo” antitumor activity of interleukin-21 on tumor eradication in different cancers in mice is illustrated

Modeling sphingomyelin synthase 1 driven reaction at the Golgi apparatus can explain data by inclusion of a positive feedback mechanism

AbstractHere we present a minimal mathematical model for the sphingomyelin synthase 1 (SMS1) driven conversion of ceramide to sphingomyelin based on chemical reaction kinetics. We demonstrate via mathematical analysis that this model is not able to qualitatively reproduce experimental measurements on lipid compositions after altering SMS1 activity. We prove that a positive feedback mechanism from the products to the reactants of the reaction is one possible model extension to explain these specific experimental data. The proposed mechanism in fact exists in vivo via protein kinase D and the ceramide transfer protein CERT. The model is further evaluated by additional observations from the literature

Long-Acting GLP-1 Receptor Agonist Exenatide Influence on the Autonomic Cardiac Sympatho-Vagal Balance

Long-acting glucagon-like peptide 1 receptor agonists are increasingly used to treat type 2 diabetes. An increase of heart rate (HR) has been observed with their use. To elucidate the role of the cardiac sympatho-vagal balance as a possible mediator of the reported increase in HR, we performed power spectral analysis of HR variability (HRV) in patients receiving exenatide extended-release (ER). Twenty-eight ambulatory patients with type 2 diabetes underwent evaluation at initiation of exenatide-ER and thereafter at 3 and at 6 months. To obtain spectral analyses of HRV, a computerized acquisition of 10 minutes of RR electrocardiogram intervals (mean values of ~700 RR intervals) were recorded both in lying and in standing positions. All patients showed a substantial increase of HR both in lying and in standing positions. Systolic blood pressure, body weight, and glycated hemoglobin A1c significantly decreased both at 3 and 6 months compared with basal levels. The low-frequency/high-frequency ratio varied from 3.05 \ub1 0.4 to 1.64 \ub1 0.2 (P < 0.001) after 3 months and to 1.57 \ub1 0.3 (P < 0.001) after 6 months in a lying position and from 4.56 \ub1 0.8 to 2.24 \ub1 0.3 (P < 0.001) after 3 months and to 2.38 \ub1 0.4 (P < 0.001) after 6 months in a standing position compared with basal values, respectively. HR variations, induced by exenatide-ER treatment, do not appear to be related to sympathetic autonomic tone. Of note, we observed a relative increase of vagal influence on the heart

Modelling the effects of glucagon during glucose tolerance testing.

From Europe PMC via Jisc Publications RouterHistory: ppub 2019-12-01, epub 2019-12-12Publication status: PublishedBACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted

A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

In this paper, the problem of predicting blood glucose concentrations (BG) for the treatment of patients with type 1 diabetes, is addressed. Predicting BG is of very high importance as most treatments, which consist in exogenous insulin injections, rely on the availability of BG predictions. Many models that can be used for predicting BG are available in the literature. However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability. The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed

Modeling SMS driven conversion of ceramide to sphingomyelin reveals the existence of a positive feedback mechanism

In questa tesi presentiamo un modello matematico minimo per la conversione di un ceramide in sfingomielina catalizzata dall'enzima sfingomielina sintasi 1 (SMS1) basato sulle leggi della cinetica chimica. Viene dimostrato, utilizzando tecniche di sampling per la stima parametrica e metodi di analisi matematica, che questo modello non è in grado di riprodurre qualitativamente delle misure sperimentali sulla composizioni dei lipidi in seguito ad alterazione dell'attivita enzimatica di SMS1. Concludiamo quindi che è necessario considerare un meccanismo di feedback positivo fra i prodotti e i reagenti della reazione, che esiste effettivamente in vivo tramite la proteina chinasi D e la proteina di trasporto di ceramide CERT. Di conseguenza, proponiamo un secondo modello modificato in modo da comprendere questo meccanismo di feedback, che risulta essere in grado di spiegare i risultati sperimentali // Here we present a minimal mathematical model for the Sphingomyelin synthase 1 (SMS1) driven conversion of ceramide to sphingomyelin based on chemical reaction kinetics. We demonstrate, via sampling-based parameter estimation and mathematical analysis, that this model is not able to qualitatively reproduce experimental measurements on lipid compositions after altering SMS1 activities. We conclude that a positive feedback mechanism is required from the products to the reactants of the reaction, which in fact exists in vivo via protein kinase D and the ceramide transfer protein CERT. Accordingly, a modified model that comprises this feedback mechanism was able to reproduce experimental finding