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Background: The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.

A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.

BackgroundFlux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature.ResultsWe introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods.ConclusionsWe present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.

The essential goal of biomedical research is to understand the underlying mechanism of disease development. Unfortunately, achieving this goal requires expensive and time-consuming efforts in medical biotechnology. This review focuses on how context-specific genome-scale metabolic network models may facilitate reaching this goal. Such models provide an in silico framework for computational simulation of cellular metabolism, predicting the outcome of experiments. Therefore, by using these models at the initial stages of experimental design, time and cost in biomedical researches may be reduced. Furthermore, with the availability of such models, not only important pathways involved in cell dysfunction may be better understood, but also drug targets predicted based on these models can be seen as novel targets for in vivo validation. The main point of this review is that metabolic modeling can predict drug targets and biomarkers without the need for kinetics data. We provide a comprehensive review of human metabolic models and their applications, in addition to the methods used for analyzing models. We discuss how these models have been used in analyzing metabolic capabilities of different cells and tissues, in identifying disease-related metabolic pathways and biomarkers, and in understanding the human-microbe interaction.

Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.

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