Search

The ability to design and engineer organisms demands the ability to predict kinetic responses of novel regulatory networks built from well-characterized biological components. Surprisingly, few validated kinetic models of complex regulatory networks have been derived by combining models of the network components. A major bottleneck in producing such models is the difficulty of measuring in vivo rate constants for components of complex networks. We demonstrate that a simple, genetic approach to measuring rate constants in vivo produces an accurate kinetic model of the complex network that Saccharomyces cerevisiae employs to regulate the expression of genes encoding glucose transporters. The model predicts a transient pulse of transcription of HXT4 (but not HXT2 or HXT3) in response to addition of a small amount of glucose to cells, an outcome we observed experimentally. Our model also provides a mechanistic explanation for this result: HXT2-4 are governed by a type 2, incoherent feed forward regulatory loop involving the Rgt1 and Mig2 transcriptional repressors. The efficiency with which Rgt1 and Mig2 repress expression of each HXTgene determines which of them have a pulse of transcription in response to glucose. Finally, the model correctly predicts how lesions in the feed forward loop change the kinetics of induction of HXT4 expression. gene--regulatory networks | glucose transport | kinetic model | Saccharomyces cerevisiae | systems biology doi/ 10.1073/pnas.0912483107

Blood ketone body levels increase during starvation and untreated diabetes. Here we tested the hypothesis that ketone bodies directly inhibit insulin action in skeletal muscle. We investigated the effect of D,L-[beta]-hydroxybutyrate (BOH; the major ketone body in vivo) on insulin-mediated glucose uptake (2-deoxyglucose) in isolated mouse soleus (oxidative) and extensor digitorum longus (EDL; glycolytic) muscle. BOH inhibited insulin-mediated glucose uptake in soleus (but not in EDL) muscle in a time- and concentration-dependent manner. Following 19.5 h of exposure to 5 mM BOH, insulin-mediated (20 mU/ml) glucose uptake was inhibited by ~90% (substantial inhibition was also observed in 3-O-methylglucose transport). The inhibitory effect of BOH was reproduced with D- but not L-BOH. BOH did not significantly affect hypoxia- or AICAR-mediated (activates AMP-dependent protein kinase) glucose uptake. The BOH effect did not require the presence/utilization of glucose since it was also seen when glucose in the medium was substituted with pyruvate. To determine whether the BOH effect was mediated by oxidative stress, an exogenous antioxidant (1 mM tempol) was used; however, tempol did not reverse the BOH effect on insulin action. BOH did not alter the levels of total tissue GLUT4 protein or insulin-mediated tyrosine phosphorylation of the insulin receptor and insulin receptor substrate-1 but blocked insulin-mediated phosphorylation of protein kinase B by ~50%. These data demonstrate that BOH inhibits insulin-mediated glucose transport in oxidative muscle by inhibiting insulin signaling. Thus ketone bodies may be potent diabetogenic agents in vivo. ketone bodies; soleus; extensor digitorum longus; glucose transporter 4 doi: 10.1152/ajpendo.00142.2010.

A reliable model of glucose absorption after oral ingestion may facilitate simulation as well as pathophysiological studies. One of the difficulties for the development and quality assessment of such models has been the lack of gold standard data for their validation. Thus, while data on plasma concentrations of glucose are available, the rates of appearance in plasma of ingested glucose (Ra) were not available to develop such models. Here we utilize the recent availability of Ra data, estimated with a model-independent multiple tracer technique, to formulate a system model of intestinal glucose absorption. Two published and two new models are tested on this new data set. One of the two new models performed best: it is nonlinear, describes the Ra data well and its parameters are estimated with good precision. This model has important potential both in simulation contexts, e.g., it can be incorporated in whole-body models of the glucose regulatory system, as well as in physiological and clinical studies to quantitatively characterize possible impairment of glucose absorption in particular populations such as elderly and diabetic individuals. Index Terms--Gastric emptying, gastrointestinal system, meal, OGTT, parameter estimation, physiological model.

In humans, failure to express the fragile X mental retardation protein (FMRP) gives rise to fragile X syndrome, the most common form of inherited mental retardation. A fragile X knockout (fmr1 KO) mouse has been described that has some of the characteristics of patients with fragile X syndrome, including immature dendritic spines and subtle behavioral deficits. In our behavioral studies, fmr1 KO mice exhibited hyperactivity and a higher rate of entrance into the center of an open field compared with controls, suggesting decreased levels of anxiety. Our finding of impaired performance of fmr1 KO mice on a passive avoidance task is suggestive of a deficit in learning and memory. In an effort to understand what brain regions are involved in the behavioral abnormalities, we applied the [[sup.14]C]deoxyglucose method for the determination of cerebral metabolic rates for glucose (CM[R.sub.glc]). We measured CM[R.sub.glc] in 38 regions in adult male fmr1 KO and WT littermates. We found CM[R.sub.glc] was higher in all 38 regions in fmr1 KO mice, and in 26 of the regions, differences were statistically significant. Differences in CM[R.sub.glc] ranged from 12% to 46%, and the greatest differences occurred in regions of the limbic system and primary sensory and posterior parietal cortical areas. Regions most affected are consistent with behavioral deficiencies and regions in which FMRP expression is highest. Higher CM[R.sub.glc] in fragile X mice may be a function of abnormalities found in dendritic spines.

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-[zeta] is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling. signal transduction; metabolism; insulin resistance; GLUT4

A two compartmental model of glucose metabolism by beta-cells and non-beta-cells was utilized to determine the rate of 2-deoxyglucose-6-phosphatase (DG-6-P) dephosphorylation. The measurement of DG-6-P in pancreatic islets involved the calculation of glucose cycling based on the rate of intracellular glucose phosphorylation and the rate of DG-6-P dephosphorylation. Furthermore, the effects of glucose-6-phosphatase on whole islet glucose usage were simulated by utilizing a first-order rate constant involving phosphofructokinase.

A three-compartment mammillary model was developed to exhibit glucose kinetics in sheep based on mathematical derivations from plasma data and additional sampling of thoracic duct lymph. The central compartment was identified as the plasma pool associated with rapid glucose exchange between the tissues of sheep. On the other hand, the peripheral compartment was identified as the extracellular pool associated with slow glucose exchange in the tissues of sheep.

The nonlinear mixed effects model (NONMEM) to predict glucose kinetics was developed to obtain parameter estimates for tissue insulin sensitivity and glucose-dependent tissue glucose disposal. Compared to the standard two-stage estimation model, NONMEM generated better parameter values that represent the population. An experiment that make use of NONMEM calculations is presented.

A hot minimal model which utilizes a stable isotopically labeled insulin-modified intravenous glucose tolerance test was used to assess insulin sensitivity in non-insulin-dependent diabetes mellitus (NIDDM) subjects. The results were compared to those of the cold indexes to gain further insight regarding glucose and insulin effect. The comparison indicated that hot indexes are more reliable and efficient for assessing glucose tolerance in NIDDM subjects.

A multicompartment tracer model is used to quantify transmembrane glucose flux and intracellular glucose phosphorylation in human muscles. The model is based on the movement of three tracers in a vein of the human forearm and a computerized multicompartment study of the tracer washout curves. Glucose transport is determined by the difference in the kinetics of the tracers. The results of the model indicate that physiological hyperinsulinemia affects glucose metabolism by stimulating transport and phosphorylation, and enghances glucose uptake in human skeletal muscle. The structure and procedure for developing the tracer model are discussed.

A glucose kinetic model, containing insulin-dependent and independent pathways of glucose uptake and a saturating flux term for glucose transport into cells by the glucose transporters, estimated the glucose utilization flux in diabetic patients and control subjects using a bolus of (3-H3) glucose. It was indicated that high plasma glucose concentrations in diabetic patients are due to a high glucose production rate by the liver resulting in reduction in glucose metabolic clearance rate due to relative saturation of the available pool of glucose transporters.

A mathematical model of beta-cell glucose metabolism and insulin release in the pancreatic beta-cell quantitatively proves that glucokinase controls the glucose metabolism rate and the rate of insulin secretion, and is the glucosensor of the beta-cell. The mechanisms of the model are discussed in detail.

Duna Massillon et al.'s estimations on glucose-6-phosphatase flux reverts to the Steele model during their calculations of the net glucose balance. Their conventional equations were erroneous due to low levels of liver glycogen in the mice that were used during the experiment. However, Massillon et al. believe that the the use of the Steele model under steady-state conditions produces less accurate results. Furthermore, hepatic glucose production cannot be measured by conventional tracer methodologies.