Modeling a simplified regulatory system of blood glucose at molecular levels

Liu, Weijiu; Tang, Fusheng

doi:10.1016/j.jtbi.2008.02.021

Cited by 70 publications

(86 citation statements)

References 42 publications

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“…Many engineers have explored the use of real time control for managing diabetes: Finan et al 22 explored the effect of exciting a range of inputs on empirical dynamic models for type 1 diabetes to explore the sensitivity and safety of use, Zavitsanou et al 23 modeled the insulin delivery system, Semizer et al 24 compared control algorithms for virtual patients with an artificial pancreas and Percival et al 25 developed a practical control approach in an artificial pancreatic beta cell. Liu et al 26,27 modelled the dynamics of the insulin signaling pathway while Cardenas and Goldbeter 28 explored a specific switch in the metabolism, between glucose phosphorylase and glycogen synthase. Life scientists have developed great expertize in understanding and modeling metabolic signaling behavior.…”

Section: Modeling and Analysis Of Basic Liver System Functionmentioning

confidence: 99%

Liver function as an engineering system

et al. 2016

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Process Systems Engineering has tackled a wide range of problems including manufacturing, the environment, and advanced materials design. Here we discuss how tools can be deployed to tackle medical problems which involve complex chemical transformations and spatial phenomena looking in particular at the liver system, the body's chemical factory. We show how an existing model has been developed to model distributed behavior necessary to predict the behavior of drugs for treating liver disease. The model has been used to predict the effects of suppression of de novo lipogenesis, stimulation of b-oxidation and a combination of the two. A reduced model has also been used to explore the prediction of behavior of hormones in the blood stream controlling glucose levels to ensure that levels are kept within safe bounds using interval methods. The predictions are made resulting from uncertainty in two key parameters with oscillating input resulting from regular feeding. V C 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3285-3297, 2016 Keywords: mathematical modeling, biomedical engineering, systems biology, medical, controlThe liver, its regulation, and its diseases Why systems engineering of the liver?Systems Engineering is the discipline of the management of complex engineering systems over their whole life cycle. Process Systems Engineering is the study of complex process engineering systems involving chemical and physical change. Prof Roger Sargent was a pioneer from the 1950s in seeing the potential that computers could have to revolutionize the way that we tackled these problems.1 He also saw the way Chemical Engineering was broadening its base into molecular and biological systems to improve manufacturing. 1,2The liver is one part of a very complex processing system which ensures that all parts of the body receive the energy and nutrients that they need, that waste products are removed efficiently from the various streams, and that short and long term well being are maintained. It is the body's central chemical processing organ. Considering the liver system as one which controls chemical and physical change within the body, particularly of nutrition, makes it a legitimate area of study for Process Systems Engineers. A number of Chemical Engineers have also contributed to the use for Process Systems Engineering to a range of medical applications. Bogle 3 reviews recent

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Section: Modeling and Analysis Of Basic Liver System Functionmentioning

confidence: 99%

Liver function as an engineering system

et al. 2016

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“…Chew et al [205] have linked the Sedaghat insulin signaling model [142] with an early model for whole-body glucose homeostasis [206]. Also, in another effort the Sedaghat model has been linked to models for plasma insulin and glucagon concentrations, insulin and glucagon signaling, and glucose production and utilization [207,208]. However, these modeling projects are using the Sedaghat model, and the Sedaghat model is based on non-human and non-dynamic data.…”

Section: Mathematical Models Linking Insulin Signaling To Glucose Conmentioning

confidence: 99%

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

Nyman¹

2014

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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...

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“…The idea was partially adapted and modified from Liu and Tang (2008) and Kovács et al (2011). The idea is to take uncertain parameters in the nonlinear model.…”

Section: H ∞ Control Designmentioning

confidence: 99%

Model-based Angiogenic Inhibition of Tumor Growth using Modern Robust Control Method

Szeles

Sápi

Drexler

et al. 2012

IFAC Proceedings Volumes

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Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted towards molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Anti-angiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. (1999), and it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used in order to describe a continuous infusion therapy. To generalize individualized therapies a robust control method is proposed using H ∞ methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method.

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Modeling a simplified regulatory system of blood glucose at molecular levels

Cited by 70 publications

References 42 publications

Liver function as an engineering system

Liver function as an engineering system

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

Model-based Angiogenic Inhibition of Tumor Growth using Modern Robust Control Method

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