The enzyme fumarase catalyzes the reversible hydration of fumarate to malate. The reaction catalyzed by fumarase is critical for cellular energetics as a part of the tricarboxylic acid cycle, which produces reducing equivalents to drive oxidative ATP synthesis. A catalytic mechanism for the fumarase reaction that can account for the kinetic behavior of the enzyme observed in both isotope exchange studies and initial velocity studies has not yet been identified. In the present study, we develop an 11-state kinetic model of the enzyme based on the current consensus on its catalytic mechanism and design a series of experiments to estimate the model parameters and identify the major flux routes through the mechanism. The 11-state mechanism accounts for competitive binding of inhibitors and activation by different anions, including phosphate and fumarate. The model is identified from experimental time courses of the hydration of fumarate to malate obtained over a wide range of buffer and substrate concentrations. Further, the 11-state model is found to effectively reduce to a five-state model by lumping certain successive steps together to yield a mathematically less complex representation that is able to match the data. Analysis suggests the primary reaction route of the catalytic mechanism, with fumarate binding to the free unprotonated enzyme and a proton addition prior to malate release in the fumarate hydration reaction. In the reverse direction (malate dehydration), malate binds the protonated form of the enzyme, and a proton is generated before fumarate is released from the active site.The fumarate hydration reaction is the seventh step of the tricarboxylic acid (TCA) cycle, which is the biochemical pathway responsible for oxidizing acetyl-coenzyme A (an end product of carbohydrate and fatty acid oxidation) to generate reducing equivalents to drive the synthesis of ATP in aerobic metabolism. Although fumarase (also called fumarate hydratase) plays a critical role in cellular energy metabolism, a consensus catalytic mechanism that can explain the available kinetic data on the fumarase reaction is yet to be determined. The need for a quantitative understanding of this enzyme's catalytic mechanism is magnified by recent studies that link variability in this enzyme's genetic sequence with altered metabolic function in rat models of hypertension and cardiovascular disease (1).A number of putative mechanisms for mammalian heartderived and yeast fumarase have emerged from kinetic studies conducted over more than 50 years. Based on isotope exchange studies, Hansen et al. (2) proposed the five-state mechanism with six elementary reaction steps shown in Fig. 1A. In this proposed mechanism, a hydrogen ion and fumarate molecule associate with the enzyme in random order. The next steps in the hydration reaction (conversion of fumarate to malate) involve the addition of a hydroxyl group and dissociation of malate. Rose et al. (3) showed that both protonated and unprotonated forms of the unbound enzyme are able to associat...

The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).

Our knowledge of human brain evolution primarily relies on the interpretation of palaeoneurological evidence. In this context, an endocast or replica of the inside of the bony braincase can be used to reconstruct a timeline of cerebral changes that occurred during human evolution, including changes in topographic extension and structural organisation of cortical areas. These changes can be tracked by identifying cerebral imprints, particularly cortical sulci. The description of these crucial landmarks in fossil endocasts is, however, challenging. High-resolution imaging techniques in palaeoneurology offer new opportunities for tracking detailed endocranial neural characteristics. In this study, we use high-resolution imaging techniques to document the variation in extant human endocranial sulcal patterns for subsequent use as a platform for comparison with the fossil record. We selected 20 extant human crania from the Pretoria Bone Collection (University of Pretoria, South Africa), which were detailed using X-ray microtomography at a spatial resolution ranging from 94 to 123 lm (isometric). We used ENDEX to extract, and MATLAB to analyse the cortical imprints on the endocasts. We consistently identified superior, middle and inferior sulci on the frontal lobe; and superior and inferior sulci on the temporal lobe. We were able to label sulci bordering critical functional areas such as Broca's cap. Mapping the sulcal patterns on extant endocasts is a prerequisite for constructing an atlas which can be used for automatic sulci recognition.