A Model of NEFA Dynamics with Focus on the Postprandial State

Jelić, Katarina; Hallgreen, Christine Erikstrup; Colding‐Jørgensen, Morten

doi:10.1007/s10439-009-9738-6

Cited by 34 publications

(57 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…New simulations with spline approximations replacing equations (26) and (27) show better agreement with plasma NEFA (a major determinant of liver lipids). Under these conditions, hepatic TAG differs significantly in the fasted period (Figure 6), showing an emptying of lipid in the liver, rather than the build up of lipid seen in the initial simulation.…”

Section: Data Fitting Of 24 Hours Meal Patternmentioning

confidence: 93%

“…k bl : Plasma NEFA half life estimates vary from 30s to 3min [27], giving a total degradation rate of 1.3863 to 0.2310min − 1. We choose 0.4510min −1 calculated from setting fasting NEFA to 0.5mmol/l.…”

Section: Description Variable Steady Value Referencementioning

confidence: 99%

“…Previous work on whole body models of metabolism has included some, but not all, of the features we would like to include in a model focused on hepatic lipid metabolism. Jelic et al [27] produce a model focusing on NEFA dynamics which, from prescribed insulin profiles of an OGTT, accurately predict true NEFA concentrations. Man et al [41] consider muscle and adipose tissue together as peripheral tissues, along with liver and pancreatic beta cells, but do not include a fat element to their model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical modelling of hepatic lipid metabolism

Pratt

Wattis

Salter

2015

Mathematical Biosciences

View full text Add to dashboard Cite

The aim of this paper is to develop a mathematical model capable of simulating the metabolic response to a variety of mixed meals in fed and fasted conditions with particular emphasis placed on the hepatic triglyceride element of the model. Model validation is carried out using experimental data for the ingestion of three mixed composition meals over a 24-hour period. Comparison with experimental data suggests the model predicts key plasma lipids accurately given a prescribed insulin profile. One counter-intuitive observation to arise from simulations is that liver triglyceride initially decreases when a high fat meal is ingested, a phenomenon potentially explained by the carbohydrate portion of the meal raising plasma insulin.

show abstract

Section: Data Fitting Of 24 Hours Meal Patternmentioning

confidence: 93%

Section: Description Variable Steady Value Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical modelling of hepatic lipid metabolism

Pratt

Wattis

Salter

2015

Mathematical Biosciences

View full text Add to dashboard Cite

show abstract

“…Another aspect of the lipolysis control is that fluxes of fatty acids to and from adipocytes contribute to the whole-body energy homeostasis. To be able to handle the effect of these fluxes, we have started a project to extend the whole-body model for glucose control [195] with models of glucose and fatty acid dynamics [212], and of insulinregulated fatty acid dynamics in response to a meal [213]. A new version of the whole-body model will thus include glucose, insulin, and fatty acid dynamics.…”

Section: General Discussion and Future Perspectivesmentioning

confidence: 99%

Insulin signaling dynamics in human adipocytes : Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes

Nyman¹

2014

View full text Add to dashboard Cite

Type 2 diabetes is characterized by raised blood glucose levels caused by an insufficient insulin control of glucose homeostasis. This lack of control is expressed both through insufficient release of insulin by the pancreatic beta-cells, and through insulin resistance in the insulin-responding tissues. We find insulin resistance of the adipose tissue particularly interesting since it appears to influence other insulin-responding tissues, such as muscle and liver, to also become insulin resistant.The insulin signaling network is highly complex with cross-interacting intermediaries, positive and negative feedbacks, etc. To facilitate the mechanistic understanding of this network, we obtain dynamic, information-rich data and use model-based analysis as a tool to formally test different hypotheses that arise from the experimental observations. With dynamic mathematical models, we are able to combine knowledge and experimental data into mechanistic hypotheses, and draw conclusions such as rejection of hypotheses and prediction of outcomes of new experiments.We aim for an increased understanding of adipocyte insulin signaling and the underlying mechanisms of the insulin resistance that we observe in adipocytes from subjects diagnosed with type 2 diabetes. We also aim for a complete picture of the insulin signaling network in primary human adipocytes from normal and diabetic subjects with a link to relevant clinical parameters: plasma glucose and insulin. Such a complete picture of insulin signaling has not been presented before. Not for adipocytes and not for other types of cells.In this thesis, I present the development of the first comprehensive insulin signaling model that can simulate both normal and diabetic data from adipocytes -and that is linked to a whole-body glucose-insulin model. In the linking process we conclude that at least two glucose uptake parameters differ between the in vivo and in vitro conditions (Paper I). We also perform a model analysis of the early insulin signaling dynamics in rat adipocytes and conclude that internalization is important for an apparent reversed order of phosphorylation seen in these cells (Paper II). In the development of the first version of the comprehensive insulin signaling model, we introduce a key parameter for the diabetic state -an attenuated feedback (Paper III). We finally continue to build on the comprehensive model and include signaling to nuclear transcription via ERK and report substantial crosstalk in the insulin signaling network (Paper IV). Populärvetenskaplig sammanfattningTyp 2-diabetes är en vanligt förekommande sjukdom där kroppens vävnader förlorar sin känslighet mot hormonet insulin. Insulinets uppgift är att hålla en jämn blodsockernivå dygnet runt. Insulin utsöndras i samband med måltider och påverkar muskel-och fettvävnader att ta upp socker och levern att sluta producera socker. Det är viktigt att hålla en jämn blodsockernivå eftersom låga nivåer kan vara direkt dödligt och höga nivåer är skadligt för blodkärlen, vilket i sin tur kan led...

show abstract

“…This situation exemplifies the need for an increased understanding of the different timescales involved in the control of metabolism, and in an understanding of the dysregulations that occur in T2D. Models have been applied to analyse data for postprandial fatty acid fluxes [43] and steady-state relations between glucose and fatty acids in rest and exercise [44,45]. In a more comprehensive approach, Sips et al [46] used mathematical modelling to translate the information from clamp study data to physiological conditions, and could thus quantify the importance of fatty acids for postprandial glucose control.…”

Section: The Interplay Between Glucose and Fatty Acid Homeostasismentioning

confidence: 99%

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

et al. 2016

View full text Add to dashboard Cite

One contribution of 12 to a theme issue 'The Human Physiome: a necessary key to the creative destruction of medicine'. We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

show abstract

A Model of NEFA Dynamics with Focus on the Postprandial State

Cited by 34 publications

References 73 publications

Mathematical modelling of hepatic lipid metabolism

Mathematical modelling of hepatic lipid metabolism

Insulin signaling dynamics in human adipocytes : Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

Contact Info

Product

Resources

About