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Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way., (Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Motivation: Given the growth of large-scale protein–protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein–protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity.Results: DualAligner performs dual network alignment, in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach.Availability and implementation: http://www.cais.ntu.edu.sg/∼assourav/DualAligner/Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract: For acute, chronic, or hereditary diseases of the liver, cell transplantation therapies can stimulate liver regeneration or serve as a bridge until liver transplantation can be performed. Recently, fetal hepatocytes, stem cells, liver progenitor cells, or other primitive and proliferative cell types have been employed for cell transplantation therapies, in an effort to improve the survival, proliferation, and engraftment of the transplanted cells. Reviewing earlier studies, which achieved success by transplanting mature hepatocytes, we propose that there is a switch-like regulation of liver regeneration that changes state according to a stimulus threshold of extracellular influences such as cytokines, matrices and neighboring cells. Important determinants of a successful clinical outcome include sufficient quantities and functional levels of the transplanted cells (even for short periods to alter the environment), rather than just engraftment levels or survival durations of the exogenously transplanted cells. The relative importance of these determining factors will impact future choices of cell sources, delivery vehicles, and sites of cell transplantation to stimulate liver regeneration for patients with severe liver diseases. [Copyright &y& Elsevier]