Molecular biology. HNFs--linking the liver and pancreatic islets in diabetes.
Department of Cell and Molecular Physiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA.Science (Impact Factor: 31.48). 03/2004; 303(5662):1311-2. DOI: 10.1126/science.1095486
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ABSTRACT: Abstract Several types of malignant epithelial liver tumors are recognized in infants, children and young adolescents. These neoplasms mainly include hepatoblastoma and its types and subtypes, whereas hepatocellular carcinoma is very rare in this age group. A pathology classification of hepatoblastomas has been worked out over the years, although some of the subtypes listed are still not sufficiently defined. Moreover, reviews of cases within large clinical trials have shown that an increasing number of pediatric hepatic tumors deviate from the phenotypes found in current classifications. The present overview refers to some of these apparently novel and intriguing lesions.Journal of Gastroenterology and Hepatology 12/2004; 19. DOI:10.1111/j.1440-1746.2004.03706.x · 3.63 Impact Factor
Chapter: Insulin Action in the Islet β-Cell[Show abstract] [Hide abstract]
ABSTRACT: The techniques that allow spatio-temporal control of gene deletion or gene expression in transgenic and knockout animals have been useful to directly evaluate the roles of the insulin and IGF-1 receptors and proteins in their signaling pathway in islet cells. While a functional role for insulin signaling in islet β-cells is now well established, the precise pathways and proteins that mediate β-cell growth and function during development and adulthood are continuing to be unraveled. This chapter will focus on recent studies derived from using transgenic, knockout and siRNA techniques to directly examine the role of insulin and IGF-I in the regulation of development, growth and function of islet β-cells.10/2007: pages 133-151;
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ABSTRACT: The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models.PLoS Computational Biology 11/2013; 9(11):e1003306. DOI:10.1371/journal.pcbi.1003306 · 4.83 Impact Factor
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