Predicting drug-protein interactions by preserving the graph information of multi source data.

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation.

C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell.

C metabolic flux analysis (C-MFA) has emerged as a forceful tool for quantifying metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics.

CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis.

Summary: The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner.

Three-Dimensional Graphene Enhances Neural Stem Cell Proliferation Through Metabolic Regulation.

Graphene consists of two-dimensional sp2-bonded carbon sheets, a single or a few layers thick, which has attracted considerable interest in recent years due to its good conductivity and biocompatibility. Three-dimensional graphene foam (3DG) has been demonstrated to be a robust scaffold for culturing neural stem cells (NSCs) that not only supports NSCs growth, but also maintains cells in a more active proliferative state than 2D graphene films and ordinary glass. In addition, 3DG can enhance NSCs differentiation into astrocytes and especially neurons.

A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product.

Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory.

Elementary Flux Mode Analysis Revealed Cyclization Pathway as a Powerful Way for NADPH Regeneration of Central Carbon Metabolism.

NADPH regeneration capacity is attracting growing research attention due to its important role in resisting oxidative stress. Besides, NADPH availability has been regarded as a limiting factor in production of industrially valuable compounds. The central carbon metabolism carries the carbon skeleton flux supporting the operation of NADPH-regenerating enzyme and offers flexibility in coping with NADPH demand for varied intracellular environment.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics.

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells.

Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress.

The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response.

A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress.

Background: The cellular responses of bacteria to superoxide stress can be used to model adaptation to severe environmental changes. Superoxide stress promotes the excessive production of reactive oxygen species (ROS) that have detrimental effects on cell metabolic and other physiological activities. To antagonize such effects, the cell needs to regulate a range of metabolic reactions in a coordinated way, so that coherent metabolic responses are generated by the cellular metabolic reaction network as a whole.

Increasing the accuracy of mass isotopomer analysis through calibration curves constructed using biologically synthesized compounds.

Mass isotopomer analysis is an important technique to measure the production and flow of metabolites in living cells, tissues, and organisms. This technique depends on accurate quantifications of different mass isotopomers using mass spectrometry. Constructing calibration curves using standard samples is the most universal approach to convert raw mass spectrometry measurements into quantitative distributions of mass isotopomers.

Improving metabolic flux estimation via evolutionary optimization for convex solution space.

Motivation: Flux estimation by using (13) C-labeling pattern information of metabolites is currently the only method that can give accurate, detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. In essence, it corresponds to a constrained optimization problem which minimizes a weighted distance between measured and simulated results. Characteristics, such as existence of multiple local minima, non-linear and non-differentiable make this problem a special difficulty.