Calcium is a noncompetitive inhibitor of DMT1 on the intestinal iron absorption process: empirical evidence and mathematical modeling analysis.

Iron absorption is a complex and highly controlled process where DMT1 transports nonheme iron through the brush-border membrane of enterocytes to the cytoplasm but does not transport alkaline-earth metals such as calcium. However, it has been proposed that high concentrations of calcium in the diet could reduce iron bioavailability. In this work, we investigate the effect of intracellular and extracellular calcium on iron uptake by Caco-2 cells, as determined by calcein fluorescence quenching.

Novel Symbiotic Genome-Scale Model Reveals 's Arboviral Pathogen Blocking Mechanism in Aedes aegypti.

are endosymbiont bacteria known to infect arthropods causing different effects, such as cytoplasmic incompatibility and pathogen blocking in Aedes aegypti. Although several strains have been studied, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their obligate endosymbiont nature and its pathogen blocking ability. Motivated by the potential applications on disease control, we developed a genome-scale model of two strains: Mel and the strongest Dengue blocking strain known to date: MelPop.

A systems biology approach for studying Wolbachia metabolism reveals points of interaction with its host in the context of arboviral infection.

Wolbachia are alpha-proteobacteria known to infect arthropods, which are of interest for disease control since they have been associated with improved resistance to viral infection. Although several genomes for different strains have been sequenced, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their dependency for survival. Motivated by the potential applications on disease control, we developed genome-scale models of four Wolbachia strains known to infect arthropods: wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop (Drosophila melanogaster).

Mathematical modeling of the relocation of the divalent metal transporter DMT1 in the intestinal iron absorption process.

Iron is essential for the normal development of cellular processes. This metal has a high redox potential that can damage cells and its overload or deficiency is related to several diseases, therefore it is crucial for its absorption to be highly regulated. A fast-response regulatory mechanism has been reported known as mucosal block, which allows to regulate iron absorption after an initial iron challenge.

Parameter estimation and mathematical modeling for the quantitative description of therapy failure due to drug resistance in gastrointestinal stromal tumor metastasis to the liver.

In this work we develop a general mathematical model and devise a practical identifiability approach for gastrointestinal stromal tumor (GIST) metastasis to the liver, with the aim of quantitatively describing therapy failure due to drug resistance. To this end, we have modeled metastatic growth and therapy failure produced by resistance to two standard treatments based on tyrosine kinase inhibitors (Imatinib and Sunitinib) that have been observed clinically in patients with GIST metastasis to the liver. The parameter identification problem is difficult to solve, since there are no general results on this issue for models based on ordinary differential equations (ODE) like the ones studied here.

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming.

Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production.

Iron-induced reactive oxygen species mediate transporter DMT1 endocytosis and iron uptake in intestinal epithelial cells.

Recent evidence shows that iron induces the endocytosis of the iron transporter dimetal transporter 1 (DMT1) during intestinal absorption. We, and others, have proposed that iron-induced DMT1 internalization underlies the mucosal block phenomena, a regulatory response that downregulates intestinal iron uptake after a large oral dose of iron. In this work, we investigated the participation of reactive oxygen species (ROS) in the establishment of this response.

Comparative metabolic analysis of CHO cell clones obtained through cell engineering, for IgG productivity, growth and cell longevity.

Cell engineering has been used to improve animal cells' central carbon metabolism. Due to the central carbon metabolism's inefficiency and limiting input of carbons into the TCA cycle, key reactions belonging to these pathways have been targeted to improve cultures' performance. Previous works have shown the positive effects of overexpressing PYC2, MDH II and fructose transporter.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools.

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks.

Mathematical modeling of the dynamic storage of iron in ferritin.

Background: Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption.

Modeling heterocyst pattern formation in cyanobacteria.

Background: To allow the survival of the population in the absence of nitrogen, some cyanobacteria strains have developed the capability of differentiating into nitrogen fixing cells, forming a characteristic pattern. In this paper, the process by which cyanobacteria differentiates from vegetative cells into heterocysts in the absence of nitrogen and the elements of the gene network involved that allow the formation of such a pattern are investigated.

Methods: A simple gene network model, which represents the complexity of the differentiation process, and the role of all variables involved in this cellular process is proposed.

Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells.

Low temperature culture (33 degrees C) has been shown to enhance the specific productivity of recombinant antibodies in Chinese hamster ovary (CHO) cells but did not affect antibody productivity in hybridoma (MAK) cells. We probed the transcriptional response of both cells undergoing temperature shift using cDNA microarrays. Among the orthologous gene probes, common trends in the expression changes between CHO and MAK are not prominent.

Non-linear reduction for kinetic models of metabolic reaction networks.

Kinetic models of metabolic networks are essential for predicting and optimizing the transient behavior of cells in culture. However, such models are inherently high dimensional and stiff due to the large number of species and reactions involved and to kinetic rate constants of widely different orders of magnitude. In this paper we address the problem of deriving non-stiff, reduced-order non-linear models of the dominant dynamics of metabolic networks with fast and slow reactions.