Computational method for inferring objective function of glycerol metabolism in Klebsiella pneumoniae

Flux balance analysis (FBA) is an effective tool in the analysis of metabolic network. It can predict the flux distribution of engineered cells, whereas the accurate prediction depends on the reasonable objective function. In this work, we propose two nonlinear bilevel programming models on anaerobic glycerol metabolism in Klebsiella pneumoniae (K. pneumoniae) for 1,3-propanediol (1,3-PD) production. One intends to infer the metabolic objective function, and the other is to analyze the robustness of the objective function. In view of the models' characteristic an improved genetic algorithm is constructed to solve them, where some techniques are adopted to guarantee all chromosomes are feasible and move quickly towards the global optimal solution. Numerical results reveal some interesting conclusions, e.g., biomass production is the main force to drive K. pneumoniae metabolism, and the objective functions, which are obtained in term of several different groups of flux distributions, are similar.     

基元通量模式预测酵母生长现象

本文通过EFM预测了基因突变后的酵母细胞生长现象, 模拟预测结果和实验结果吻合很好; 与FBA方法得到的模拟结果相比较, EFM方法能更好地把基因突变和其表型(生长)联系起来.     

Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models

The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model.A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.     

SPECIES DIFFERENCES IN RESPONSE TO PHOTOHEMOLYTIC AGENTS

Species differences in red blood cell susceptibility to the photohemolytic agents chlorpromazine, menadione and tetracycline were examined in mouse, rat, dog, and human blood. Menadione and tetracycline (25 microM) hemolyzed mouse but not dog, rat, or human red blood cells (RBC) when irradiated with UV light but not in the dark. Chlorpromazine (25 microM) produced a photohemolytic response in all four species with mouse and rat RBC lysing fastest followed by human then dog cells. Investigations into the nature of these species differences suggested that the size of mouse RBC may contribute to its high sensitivity to photohemolytic agents. An investigation of the effect of UV light on key antioxidant enzymes revealed species differences in enzyme inactivation. These data suggest that mouse RBC may be particularly vulnerable to phototoxic agents, especially those compounds which produce active oxygen species and, therefore, may prove more useful than human RBC as a model for predicting phototoxic potential of some chemical entities.     

The NMR added value to the green foodomics perspective: Advances by machine learning to the holistic view on food and nutrition

Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modeling a necessity. High-throughput techniques, such as nuclear magnetic resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modeling food–human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients; detect biomarkers of intake and study how they impact the metabolism of different individuals; study the kinetics of compounds in foods or their by-products to detect pathological conditions; and improve the efficiency of in silico models of the metabolic network.     

Heat flux and the calorimetric-respirometric ratio as measures of catabolic flux in mammalian cells

It is advocated that cellular heat flow rate (Ø = dQ/dt, where Q is heat) be expressed as an intensive quantity specific to cell size (X) and termed heat flux (J_Ø/X). It has been the practice to cite such data on a ‘per cell’ basis, but it would be preferable to use biomass (cellular volume or mass). This quantity is shown to be a measure of metabolic activity and, more accurately, catabolic rate coupled to the demand for ATP in anabolic processes and work in the cell. Recent developments in flow microcalorimetry and dielectric spectroscopy reveal that heat flux can be measured on-line, with the potential of industrial use as a control variable in the growth of hybridoma and genetically engineered cells. This is because the enthalpy change of growth can be regarded as a unique kind of stoichiometric coefficient directly related to the mass coefficients in the growth reaction. This can be verified by an enthalpy balance comparing data for material fluxes of catabolites with the value for heat flux. Information revealed by the stoichiometric growth equation can be used to improve medium design.

The ratio of heat flux to oxygen consumption (flux) is known as the calorimetric-respirometric (CR) ratio. It detects anaerobic processes when the value is more negative than −450 (±5%) kJ mol⁻¹ O₂. These processes are found in cells growing under fully aerobic conditions, because glycolysis provides biosynthetic precursors with lactate as the byproduct. It is suggested that the CR ratio would be a powerful on-line control variable for the growth of animal cells in bioreactors.     

Colored Corn: An Up-Date on Metabolites Extraction,Health Implication,and Potential Use

Colored (orange, pink, red, purple, and blue) corn strongly attracted attention on its healthy properties mainly due to its anthocyanin and carotenoid composition which is also responsible for its pigmentation. The present review summarized the recent updates on the extraction and chemical characterization of the main plant secondary metabolites present in colored seeds, kernel, cob, husk, and silk. The main approaches used to stabilize the extracts have been discussed as well as their food and non-food uses. Both in vitro and in vivo (animal models) studies on the different effects (antibacterial, antimutagenic, antioxidant, and anti-inflammatory activities, effects on metabolic syndrome, diabetes, glucose and lipidic metabolism, and neuroprotection) of pigmented extracts on animal and human health have been summarized.     

Toxic Metal Species and ‘Endogenous’ Metalloproteins at the Blood–Organ Interface: Analytical and Bioinorganic Aspects

Globally, human exposure to environmental pollutants causes an estimated 9 million deaths per year and it could also be implicated in the etiology of diseases that do not appear to have a genetic origin. Accordingly, there is a need to gain information about the biomolecular mechanisms that causally link exposure to inorganic environmental pollutants with distinct adverse health effects. Although the analysis of blood plasma and red blood cell (RBC) cytosol can provide important biochemical information about these mechanisms, the inherent complexity of these biological matrices can make this a difficult task. In this perspective, we will examine the use of metalloentities that are present in plasma and RBC cytosol as potential exposure biomarkers to assess human exposure to inorganic pollutants. Our primary objective is to explore the principal bioinorganic processes that contribute to increased or decreased metalloprotein concentrations in plasma and/or RBC cytosol. Furthermore, we will also identify metabolites which can form in the bloodstream and contain essential as well as toxic metals for use as exposure biomarkers. While the latter metal species represent useful biomarkers for short-term exposure, endogenous plasma metalloproteins represent indicators to assess the long-term exposure of an individual to inorganic pollutants. Based on these considerations, the quantification of metalloentities in blood plasma and/or RBC cytosol is identified as a feasible research avenue to better understand the adverse health effects that are associated with chronic exposure of various human populations to inorganic pollutants. Exposure to these pollutants will likely increase as a consequence of technological advances, including the fast-growing applications of metal-based engineering nanomaterials.     

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.     

Role of Human Liver Microsomes in In Vitro Metabolism of Drugs—A Review

Drug metabolism studies are essential and necessary during the evaluation of drugs. This review discusses the in vitro human liver models to estimate the drug metabolic fates in vivo. Different approaches are provided and emphasis is placed on the potential of human liver microsomes for drug metabolism and inhibition studies. The methodology for these studies using human liver microsomes, applications of human liver microsomes, and the drugs studied by human liver microsomes are listed. Human liver microsomes represent a critical experimental model for the evaluation of drug metabolites with a high probability of clinical success.