A continuum receptor model of hepatic lipoprotein metabolism

A mathematical model describing the uptake of low density lipoprotein (LDL) and very low density lipoprotein (VLDL) particles by a single hepatocyte cell is formulated and solved. The model includes a description of the dynamic change in receptor density on the surface of the cell due to the binding and dissociation of the lipoprotein particles, the subsequent internalisation of bound particles, receptors and unbound receptors, the recycling of receptors to the cell surface, cholesterol dependent de novo receptor formation by the cell and the effect that particle uptake has on the cell's overall cholesterol content. The effect that blocking access to LDL receptors by VLDL, or internalisation of VLDL particles containing different amounts of apolipoprotein E (we will refer to these particles as VLDL-2 and VLDL-3) has on LDL uptake is explored. By comparison with experimental data we find that measures of cell cholesterol content are important in differentiating between the mechanisms by which VLDL is thought to inhibit LDL uptake. We extend our work to show that in the presence of both types of VLDL particle (VLDL-2 and VLDL-3), measuring relative LDL uptake does not allow differentiation between the results of blocking and internalisation of each VLDL particle to be made. Instead by considering the intracellular cholesterol content it is found that internalisation of VLDL-2 and VLDL-3 leads to the highest intracellular cholesterol concentration. A sensitivity analysis of the model reveals that binding, unbinding and internalisation rates, the fraction of receptors recycled and the rate at which the cholesterol dependent free receptors are created by the cell have important implications for the overall uptake dynamics of either VLDL or LDL particles and subsequent intracellular cholesterol concentration. (C) 2008 Elsevier Ltd. All rights reserved.

Identification of hepatic molecular mechanisms of action of alpha-tocopherol using global gene expression profile analysis in rats

The recent discovery that vitamin E (VE) regulates gene activity at the transcriptional level indicates that VE may exert part of its biological effects by mechanisms which may be independent of its well-recognised antioxidant function. The objective of this study was the identification of hepatic vitamin E-sensitive genes and examination of the effects of VE on their corresponding biological endpoints. Two groups of male rats were randomly assigned to either a VE-sufficient diet or to a control diet deficient in VE for 290 days. High-density oligonucleotide microarrays comprising over 7000 genes were used to assess the transcriptional response of the liver. Differential gene expression was monitored over a period of 9 months, at four different time-points, and rats were individually profiled. This experimental strategy identified several VE-sensitive genes, which were chronically altered by dietary VE. VE supplementation down-regulated scavenger receptor CD36, coagulation factor IX and 5-alpha-steroid reductase type 1 mRNA levels while hepatic gamma glutamyl-cysteinyl synthetase was significantly up-regulated. Measurement of the corresponding biological endpoints such as activated partial thromboplastin time, plasma dihydrotestosterone and hepatic glutathione substantiated the gene chip data which indicated that dietary VE plays an important role in a range of metabolic processes within the liver. (C) 2004 Elsevier B.V. All rights reserved.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Differences in glucose-dependent insulinotropic polypeptide hormone and hepatic lipase in subjects of southern and northern Europe: implications for postprandial lipaemia

The aim was to determine in 32 healthy young men from northern and southern Europe whether differences in the secretion of insulin and glucose-dependent insulinotropic polypeptide (GIP) might explain these findings through the actions of these hormones on lipoprotein lipase. In a randomized, single-blind, crossover study the effects of 2 test meals of identical macronutrient composition but different saturated fatty acid (SFA) and monounsaturated fatty acid (MUFA) contents were investigated on postprandial GIP, insulin, the ratio of incremental triacylglycerol to apolipoprotein B-48 (a marker of chylomicron size), and the activity of postheparin lipases. Fasting and postprandial GIP concentrations and postheparin hepatic lipase (HL) activities were higher in the southern Europeans (P<0.001 and P<0.02, respectively). Lipoprotein lipase activity after the SFA-rich meal was higher in the northern Europeans (P<0.01). HL activity 9 h after the SFA-rich meal and the area under the curve (AUC) for the postprandial insulin response correlated with the AUC for the postprandial GIP response (r=0.44 (P<0.04) and r=0.46 (P<0.05), respectively). There were no significant differences in chylomicron size between the 2 groups for either meal, but when the groups were combined there was a difference in chylomicron size between the SFA- and MUFA-rich meals (P<0.05), which could be due to the formation of larger chylomicrons after the MUFA-rich meal. The significantly higher GIP and insulin responses and HL activities in southern Europeans may provide an explanation for a previous report of attenuated postprandial triacylglycerol and apolipoprotein B-48 responses in them.

Effects of dietary fatty acid composition on basal and hormonestimulated hepatic lipogenesis and on circulating lipids in the rat

Thirty male rats were randomly assigned to one of three dietary groups in which the source of dietary fat was either a mixed oil, maize oil or fish oil. Effects of dietary fatty acid composition on in virro rates of [U-'4C]glucose incorporation into hepatic total lipids and into hepatic triacylglycerol were measured under basal, insulin (4 nM)-, gastric inhibitory polypeptide (GIP; 6 mi)- and insulin + GIP (4 nM + 6 n ~ ) - stimulated conditions. Effects of the three diets on postprandial plasma triacylglycerol, cholesterol, insulin and GIP concentrations were also measured. The fish-oil diet decreased rates of basal glucose incorporation into hepatic total lipids (P < 0.05) and hepatic triacylglycerol (P < 0.01) compared with the mixed-oil diet. The presence of insulin + GIP in the incubation medium stimulated glucose incorporation into hepatic total lipids in the maize-oil (P < 0.01) and fish-oil groups (P < OW), as well as into hepatic triacylglycerol in the maize-oil group (P < 0.005). In addition, the fish-oil diet decreased postprandial plasma triacylglycerol levels compared with both other dietary groups (P < 0-05 both cases), and the mixed-oil diet markedly increased postprandial plasma insulin levels compared with the other dietary groups (P c 0.001).

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Dynamic input/output linearization using recurrent neural networks

A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.