International Journal of Computational Biology and Drug Design最新文献

Biologists have been investigating plant defence response to virus infections; however, a comprehensive mathematical model of this complex process has not been developed. One obstacle in developing a dynamic model, useful for simulation, is the lack of kinetic data from which the model parameters could be determined. We address this problem by proposing a methodology for iterative improvement of the model parameters until the simulation results come close to the expectation of biology experts. These expectations are formalised in the form of constraints to be satisfied by the model simulations. In three iterative steps the model converged to satisfy the biology experts. There are two results of our approach: individual simulations and optimised model parameters, which provide a deeper insight into the biological system. Our constraint-driven optimisation approach allows for an efficient exploration of the dynamic behaviour of biological models and, at the same time, increases their reliability.

Stochastic modelling and simulation is a well-known approach for predicting the behaviour of biochemical systems. Its main applications lie in those systems wherein the inherently random fluctuations of some species are significant, as often is the case whenever just a few macromolecules have a large effect on the rest of the system. The Gillespie's stochastic simulation algorithm (SSA) is a standard method to properly realise the stochastic nature of reactions. In this paper we propose an improvement to SSA based on the Huffman tree, a binary tree which is used to define an optimal data compression algorithm. We exploit results from that area to devise an efficient search for next reactions, moving from linear time complexity to logarithmic complexity. We combine this idea with others from literature, and compare the performance of our algorithm with previous ones. Our experiments show that our algorithm is faster, especially on large models.

High cost for systematic review of biomedical literature has generated interest in decreasing overall workload. This can be done by applying natural language processing techniques to 'automate' the classification of publications that are potentially relevant for a given question. Existing solutions need training using a specific supervised machine-learning algorithm and feature-extraction system separately for each systematic review. We propose a system that only uses the input and feedback of human reviewers during the course of review. As the reviewers classify articles, the query is modified using a simple relevance feedback algorithm, and the semantically closest document to the query is presented. An evaluation of our approach was performed using a set of 15 published drug systematic reviews. The number of articles that needed to be reviewed was substantially reduced (ranging from 6% to 30% for a 95% recall).

Low exposure X-ray fluoroscopy is widely used to guide some complicate interventional procedures. Given its inherent high noise, improving the visibility of interventional devices such as stents will greatly benefit the procedures. In this study, a contrast enhancement filter has been proposed to improve stent visibility. It is designed to selectively improve the contrast of stent contour segments without dramatically accentuating quantum and clinical background noises. To achieve this, convolution directional filter banks were applied to detect the edges and orientations of stent. Next, symmetry measures were used to extract symmetrical portions of the stent image. Those information were combined to generate a partial stent contour map. The contour map was then scaled and added back to the original image to get the stent in the image contrast enhanced. It is shown that this stent enhancement filter effectively improves stent visibility in the interventional X-ray fluoroscopy.

Chemical shift-based water-fat separation method utilises water-fat resonance frequency difference to decompose signals into water and fat partitions in magnetic resonance imaging (MRI) on a pixel-wise basis. It provides an effective way to measure fat fraction, or to suppress fat signal which might obscure underlying pathology. IDEAL (Iterative decomposition of water and fat with echo asymmetry and least-squares estimation) algorithm with multi-peak fat spectral modelling has been developed. Recent studies have discussed the performance of this algorithm assuming that the frequencies and relative amplitudes of fat peaks are constant among all subjects. However, the fat spectra vary in different tissues, thus a self-calibration method which estimates the fat spectrum directly from the data provides more accurate results. In this work, we analyse the performance of multi-peak IDEAL algorithm with self-calibrated fat spectrum by theoretical calculation, simulation, and experiments, and find optimal echo time increments which provide reliable water-fat separation.

Gene Co-expression Network (GCN) analysis has been widely used for gene function and disease biomarker discovery. In this study, we present a workflow for identifying GCN associated with colon cancer metastasis. The workflow includes dense network discovery from weighted GCN followed by network activity analysis using a mutual information-based approach to identify gene networks related to metastasis. Our findings suggest several genomic regions as genetic aberrations related to colon cancer malignancy including chr11q13, 20q13, 8q24 and 14q22-23. Our work also demonstrates a novel way of interpreting gene co-expression analysis results besides functional relationships and the effectiveness of the mutual information based network analysis in detecting subtle changes between different disease states.

In this paper, a Kernel correlation coefficient (KCC) method is proposed to elucidate the gene nonlinear relationships as a distance metric. To evaluate the performance of this nonlinear distance measure, a biological network of the Gaussian Kernel on a public dataset of yeast genes is constructed by using a graph theory. Specifically, the distribution and properties of this new measure are analysed and compared with the classical Pearson correlation method. The reliability and advantages of our proposed Kernel correlation metric is verified and shown formally on ten showcases of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. Test experiment results demonstrate that the proposed Kernel correlation coefficient measure has a strong capability in identifying interaction genes, and that the proposed method can detect accurately the key genes and functional interactions (also known as the cliques) as compared to the commonly used Pearson correlation and Mutual Information measures.

P-glycoprotein (P-gp)-mediated efflux system plays an important role to maintain chemical balance in mammalian cells for endogenous and exogenous chemical compounds. However, despite the extensive characterisation of P-gp potential interaction with drug-like molecules, the interaction of carbon nanoparticles with this type of protein molecule is poorly understood. Thus, carbon nanoparticles were analysed, such as buckminsterfullerenes (C20, C60, C70), capped armchair single-walled carbon nanotube (SWCNT or C168), and P-gp interactions using different molecular docking techniques, such as gradient optimisation algorithm (ADVina), Lamarckian genetic algorithm (FastDock), and shape-based approach (PatchDock) to estimate the binding affinities between these structures. The theoretical results represented in this work show that fullerenes might be P-gp binders because of low levels of Gibbs free energy of binding (ΔG) and potential of mean force (PMF) values. Furthermore, the SWCNT binding is energetically unfavourable, leading to a total decrease in binding affinity by elevation of the residual area (Ares), which also affects the π-π stacking mechanisms. Further, the obtained data could potentially call experimental studies using carbon nanostructures, such as SWCNT for development of drug delivery vehicles, to administer and assess drug-like chemical compounds to the target cells since organisms probably did not develop molecular sensing elements to detect these types of carbon molecules.

Noise-Induced Hearing Loss (NIHL) is the most common occupational disease in the USA. Impulse noise is a typical noise exposure in military and industrial fields, and generates severe hearing loss problem in these fields. This paper presents four key parameters of impulse noise that significantly affect on Auditory Risk Unit (ARU) in the Auditory Hazard Assessment Algorithm for Humans (AHAAH) model. The results show that ARUs increases monotonically with the peak pressure (both P(+) and P(-)) increasing. While the ARUs increase first and then decrease with time durations rising, and the peak of ARUs appears at about t = 0.2 msec (for both t(+) and t(-)). In addition, the auditory hazard of measured impulse noises generated by the lab noise exposure system was evaluated by using AHAAH model. Results from experiments indicate that the AHAAH model is suitable for impulse noise hazardous evaluation.

The substrate binding loop (SBL) of inhA shows conformational changes on binding of direct inhA inhibitors (DIIs). The knowledge of conformational changes and its importance in binding of DII to inhA has not been explored before. This study initially focused on studying the conformational changes of SBL in selected inhA crystal structures. These conformational changes are measured as angle of rotation for SBL from the static hinge region, Ile194, in the crystal structures. The maximal angle difference of ∼41° was observed between most open and closed conformation of SBL. To gain insights into these conformational changes, comparative molecular dynamics simulations of inhA bound with a direct inhibitor (Genz10850) and apoprotein were performed. A considerable variation in the angle of rotation (∼24° to ∼12°) for the SBL which led to the closed conformation was observed during binding of Genz10850 with a consistent increase in electrostatic energy, whereas no change was observed in apoprotein. Hence, conformational changes in the SBL under the influence of inhibitor can be utilised as a parameter for enhanced binding inhibitor with inhA to screen the potent DIIs.