Understanding the uncertainty of estimating herbicide and nutrient mass loads in a flood event with guidance on estimator selection

The aim of this study was to understand the uncertainty of estimating loads for observed herbicides and nutrients during a flood event and provide guidance on estimator selection. A high-resolution grab sampling campaign (258 samples over 100 h) was conducted during a flood event in a tropical waterway in Queensland, Australia. Ten herbicides and three nutrient compounds were detected at elevated concentrations. Each had a unique chemograph with differences in transport processes (e.g. dependence on flow, dilution processes and timing of concentration pulses). Resampling from the data set was used to assess uncertainty. Bias existed at lower sampling efforts but depended on estimator properties as sampling effort increased: the interpolation, ratio and regression estimators became unbiased. Large differences were observed in precision and the importance of sampling effort and estimator selection depended on the relationship between the chemograph and hydrograph. The variety of transport processes observed and the resultant variability in uncertainty suggest that useful load estimates can only be obtained with sufficient samples and appropriate estimator selection. We provide a rationale to show the latter can be guided across sampling periods by selecting an estimator where the sampling regime or the relationship between the chemograph and hydrograph meet its assumptions: interpolation becomes more correct as sampling effort increases and the ratio becomes more correct as the r2 correlation between flux and flow increases (e.g. > 0.9); a stratified composite sampling approach, even with random samples, is a promising alternative

Monotone and near-monotone biochemical networks

Monotone subsystems have appealing properties as components of larger networks, since they exhibit robust dynamical stability and predictability of responses to perturbations. This suggests that natural biological systems may have evolved to be, if not monotone, at least close to monotone in the sense of being decomposable into a “small” number of monotone components, In addition, recent research has shown that much insight can be attained from decomposing networks into monotone subsystems and the analysis of the resulting interconnections using tools from control theory. This paper provides an expository introduction to monotone systems and their interconnections, describing the basic concepts and some of the main mathematical results in a largely informal fashion

Variable Selection and Interpretation in Structure-Affinity Correlation Modeling of Estrogen Receptor Binders

A computational approach for the identification and investigation of correlations between a chemical structure and a selected biological property is described. It. is based on a set of 132 compounds of known chemical structures, which were tested for their binding affinities to the estrogen receptor. Different multivariate modeling methods, i.e., partial least-squares regression, counterpropagation neural network, and error-back-propagation neural network, were applied, and the prediction ability of each model was tested in order to compare the results of the obtained models. To reduce the extensive set of calculated structural descriptors, two types of variable selection methods were applied, depending on the modeling approach used. In particular, the final partial least-squares regression model was built using the "variable importance in projection" variable selection method, while genetic algorithms were applied in neural network modeling to select the optimal set of descriptors. A thorough statistical study of the variables selected by genetic algorithms is shown. The results were assessed with the aim to get insight to the mechanisms involved in the binding of estrogenic compounds to the receptor. The variable selection oil the basis of genetic algorithm wits controlled with the test set of compounds, extracted from the data set available. To compare the predictive ability of all the optimized models, a leave-one-out cross-validation procedure was applied, the best model being the nonlinear neural network model based on error back-propagation algorithm, which resulted in R-2 = 92.2% and Q(2) = 70.8%

Impact of Mercury(II) on proteinase K catalytic center: investigations via classical and Born-Oppenheimer molecular dynamics

Mercury(II) has a strong affinity for the thiol groups in proteins often resulting in the disruption of their biological functions. In this study we present classical and first-principles, DFT-based molecular dynamics (MD) simulations of a complex of Hg(II) and proteinase K, a well-known serine protease with a very broad and diverse enzymatic activity. It contains a catalytic triad formed by Asp39, His69, and Ser224, which is responsible for its biological activity. It was found previously by X-ray diffraction experiments that the presence of Hg(II) inhibits the enzymatic action of proteinase K by affecting the stereochemistry of the triad. Our simulations predict that (i) the overall structure as well as the protein backbone dynamics are only slightly affected by the mercury cation, (ii) depending on the occupied mercury site, the hydrogen bonds of the catalytic triad are either severely disrupted (both bonds for mercury at site 1, and the His69-Ser224 contact for mercury at site 2) or slightly strengthened (the Asp39-His69 bond when mercury is at site 2), (iii) the network of hydrogen bonds of the catalytic triad is not static but undergoes constant fluctuations, which are significantly modified by the presence of the Hg(II) cation, influencing in turn the triad's ability to carry out the enzymatic function-these facts explain the experimental findings on the inhibition of proteinase K by Hg(II)

Properties of flavonoids influencing the binding to bilitranslocase investigated by neural network modelling

Bilitranslocase is a plasma membrane carrier firstly identified on the sinusoidal (vascular) domain of liver cells and later on also in the gastric epithelium. It transports diverse organic anions, such as bilirubin, some phthaleins and many dietary anthocyanins, suggesting that it could play a role both in the absorption of flavonoids from dietary sources and in their hepatic metabolism. This work was aimed at characterising the interaction of bilitranslocase with flavonols, a flavonoid sub-class. The results obtained show that, contrary to anthocyanins, flavonol glycosides do not interact with the carrier, whereas just some of the corresponding aglycones act as relatively poor ligands to bilitranslocase. These data point to a clear-cut discrimination between anthocyanins and flavonols occurring at the level of the bilitranslocase transport site. A quantitative structure-activity relationship based on counter propagation artificial neural network modelling was undertaken in order to shed light on the nature of flavonoid interaction with bilitranslocase. It was found that binding relies on the ability to establish hydrogen bonds, ruling out the involvement of charge interactions. This requisite might be at the basis of the discrimination between anthocyanins and flavonols by bilitranslocase and could lie behind some aspects of the distinct pharmacokinetic properties of anthocyanins and flavonols in mammals