BioMed Research International: Computational Biology The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Pathway Bridge Based Multiobjective Optimization Approach for Lurking Pathway Prediction Wed, 16 Apr 2014 14:08:30 +0000 Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis. Rengjing Zhang, Chen Zhao, Zixiang Xiang, and Xiaobo Zhou Copyright © 2014 Rengjing Zhang et al. All rights reserved. Gender-Specific DNA Methylome Analysis of a Han Chinese Longevity Population Mon, 14 Apr 2014 11:53:34 +0000 Human longevity is always a biological hotspot and so much effort has been devoted to identifying genes and genetic variations associated with longer lives. Most of the demographic studies have highlighted that females have a longer life span than males. The reasons for this are not entirely clear. In this study, we carried out a pool-based, epigenome-wide investigation of DNA methylation profiles in male and female nonagenarians/centenarians using the Illumina 450 K Methylation Beadchip assays. Although no significant difference was detected for the average methylation levels of examined CpGs (or probes) between male and female samples, a significant number of differentially methylated probes (DMPs) were identified, which appeared to be enriched in certain chromosome regions and certain parts of genes. Further analysis of DMP-containing genes (named DMGs) revealed that almost all of them are solely hypermethylated or hypomethylated. Functional enrichment analysis of these DMGs indicated that DNA hypermethylation and hypomethylation may regulate genes involved in different biological processes, such as hormone regulation, neuron projection, and disease-related pathways. This is the first effort to explore the gender-based methylome difference in nonagenarians/centenarians, which may provide new insights into the complex mechanism of longevity gender gap of human beings. Liang Sun, Jie Lin, Hongwu Du, Caiyou Hu, Zezhi Huang, Zeping Lv, Chenguang Zheng, Xiaohong Shi, Yan Zhang, and Ze Yang Copyright © 2014 Liang Sun et al. All rights reserved. Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis Mon, 07 Apr 2014 14:01:10 +0000 Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent. Gang Zhang, Jian Yin, Xiangyang Su, Yongjing Huang, Yingrong Lao, Zhaohui Liang, Shanxing Ou, and Honglai Zhang Copyright © 2014 Gang Zhang et al. All rights reserved. Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis Sun, 06 Apr 2014 14:11:33 +0000 Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers. Jie Huang, Zhandong Sun, Wenying Yan, Yujie Zhu, Yuxin Lin, Jiajai Chen, Bairong Shen, and Jian Wang Copyright © 2014 Jie Huang et al. All rights reserved. In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches Sun, 06 Apr 2014 08:24:04 +0000 The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels. Mariagrazia Belfiore, Marzio Pennisi, Giuseppina Aricò, Simone Ronsisvalle, and Francesco Pappalardo Copyright © 2014 Mariagrazia Belfiore et al. All rights reserved. Identification of Simple Sequence Repeat Biomarkers through Cross-Species Comparison in a Tag Cloud Representation Mon, 31 Mar 2014 14:06:47 +0000 Simple sequence repeats (SSRs) are not only applied as genetic markers in evolutionary studies but they also play an important role in gene regulatory activities. Efficient identification of conserved and exclusive SSRs through cross-species comparison is helpful for understanding the evolutionary mechanisms and associations between specific gene groups and SSR motifs. In this paper, we developed an online cross-species comparative system and integrated it with a tag cloud visualization technique for identifying potential SSR biomarkers within fourteen frequently used model species. Ultraconserved or exclusive SSRs among cross-species orthologous genes could be effectively retrieved and displayed through a friendly interface design. Four different types of testing cases were applied to demonstrate and verify the retrieved SSR biomarker candidates. Through statistical analysis and enhanced tag cloud representation on defined functional related genes and cross-species clusters, the proposed system can correctly represent the patterns, loci, colors, and sizes of identified SSRs in accordance with gene functions, pattern qualities, and conserved characteristics among species. Jhen-Li Huang, Hao-Teng Chang, Ronshan Cheng, Hui-Huang Hsu, and Tun-Wen Pai Copyright © 2014 Jhen-Li Huang et al. All rights reserved. Global Analysis of miRNA Gene Clusters and Gene Families Reveals Dynamic and Coordinated Expression Tue, 25 Mar 2014 08:34:47 +0000 To further understand the potential expression relationships of miRNAs in miRNA gene clusters and gene families, a global analysis was performed in 4 paired tumor (breast cancer) and adjacent normal tissue samples using deep sequencing datasets. The compositions of miRNA gene clusters and families are not random, and clustered and homologous miRNAs may have close relationships with overlapped miRNA species. Members in the miRNA group always had various expression levels, and even some showed larger expression divergence. Despite the dynamic expression as well as individual difference, these miRNAs always indicated consistent or similar deregulation patterns. The consistent deregulation expression may contribute to dynamic and coordinated interaction between different miRNAs in regulatory network. Further, we found that those clustered or homologous miRNAs that were also identified as sense and antisense miRNAs showed larger expression divergence. miRNA gene clusters and families indicated important biological roles, and the specific distribution and expression further enrich and ensure the flexible and robust regulatory network. Li Guo, Sheng Yang, Yang Zhao, Hui Zhang, Qian Wu, and Feng Chen Copyright © 2014 Li Guo et al. All rights reserved. Evaluation and Comparison of Multiple Aligners for Next-Generation Sequencing Data Analysis Sun, 23 Mar 2014 06:48:44 +0000 Next-generation sequencing (NGS) technology has rapidly advanced and generated the massive data volumes. To align and map the NGS data, biologists often randomly select a number of aligners without concerning their suitable feature, high performance, and high accuracy as well as sequence variations and polymorphisms existing on reference genome. This study aims to systematically evaluate and compare the capability of multiple aligners for NGS data analysis. To explore this capability, we firstly performed alignment algorithms comparison and classification. We further used long-read and short-read datasets from both real-life and in silico NGS data for comparative analysis and evaluation of these aligners focusing on three criteria, namely, application-specific alignment feature, computational performance, and alignment accuracy. Our study demonstrated the overall evaluation and comparison of multiple aligners for NGS data analysis. This serves as an important guiding resource for biologists to gain further insight into suitable selection of aligners for specific and broad applications. Jing Shang, Fei Zhu, Wanwipa Vongsangnak, Yifei Tang, Wenyu Zhang, and Bairong Shen Copyright © 2014 Jing Shang et al. All rights reserved. Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm Mon, 17 Mar 2014 07:05:39 +0000 Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC. Zhiwei Ji and Bing Wang Copyright © 2014 Zhiwei Ji and Bing Wang. All rights reserved. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics Thu, 06 Mar 2014 11:37:58 +0000 With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. Shan Li, Liying Kang, and Xing-Ming Zhao Copyright © 2014 Shan Li et al. All rights reserved. Data Analysis and Tissue Type Assignment for Glioblastoma Multiforme Mon, 03 Mar 2014 08:49:15 +0000 Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret. Yuqian Li, Yiming Pi, Xin Liu, Yuhan Liu, and Sofie Van Cauter Copyright © 2014 Yuqian Li et al. All rights reserved. Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation Tue, 11 Feb 2014 12:26:15 +0000 Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data. Then we use sparse representation classifier (SRC) to build tumor classification model. The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO. Bin Gan, Chun-Hou Zheng, Jun Zhang, and Hong-Qiang Wang Copyright © 2014 Bin Gan et al. All rights reserved. Evaluating the Influence of Motor Control on Selective Attention through a Stochastic Model: The Paradigm of Motor Control Dysfunction in Cerebellar Patient Sun, 09 Feb 2014 07:21:51 +0000 Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the “optimal motor system” and the “optimal stimulus encoders.” Giacomo Veneri, Antonio Federico, and Alessandra Rufa Copyright © 2014 Giacomo Veneri et al. All rights reserved. Detection of Epileptic Seizure Event and Onset Using EEG Wed, 29 Jan 2014 00:00:00 +0000 This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds. Nabeel Ahammad, Thasneem Fathima, and Paul Joseph Copyright © 2014 Nabeel Ahammad et al. All rights reserved. 3D Assessment of Mandibular Growth Based on Image Registration: A Feasibility Study in a Rabbit Model Thu, 02 Jan 2014 11:29:35 +0000 Background. Our knowledge of mandibular growth mostly derives from cephalometric radiography, which has inherent limitations due to the two-dimensional (2D) nature of measurement. Objective. To assess 3D morphological changes occurring during growth in a rabbit mandible. Methods. Serial cone-beam computerised tomographic (CBCT) images were made of two New Zealand white rabbits, at baseline and eight weeks after surgical implantation of 1 mm diameter metallic spheres as fiducial markers. A third animal acted as an unoperated (no implant) control. CBCT images were segmented and registered in 3D (Implant Superimposition and Procrustes Method), and the remodelling pattern described used color maps. Registration accuracy was quantified by the maximal of the mean minimum distances and by the Hausdorff distance. Results. The mean error for image registration was 0.37 mm and never exceeded 1 mm. The implant-based superimposition showed most remodelling occurred at the mandibular ramus, with bone apposition posteriorly and vertical growth at the condyle. Conclusion. We propose a method to quantitatively describe bone remodelling in three dimensions, based on the use of bone implants as fiducial markers and CBCT as imaging modality. The method is feasible and represents a promising approach for experimental studies by comparing baseline growth patterns and testing the effects of growth-modification treatments. I. Kim, M. E. Oliveira, W. J. Duncan, I. Cioffi, and M. Farella Copyright © 2014 I. Kim et al. All rights reserved. Walking on a Tissue-Specific Disease-Protein-Complex Heterogeneous Network for the Discovery of Disease-Related Protein Complexes Sat, 28 Dec 2013 11:37:39 +0000 Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases. Thibault Jacquemin and Rui Jiang Copyright © 2013 Thibault Jacquemin and Rui Jiang. All rights reserved. Improved scFv Anti-HIV-1 p17 Binding Affinity Guided from the Theoretical Calculation of Pairwise Decomposition Energies and Computational Alanine Scanning Thu, 07 Nov 2013 13:59:03 +0000 Computational approaches have been used to evaluate and define important residues for protein-protein interactions, especially antigen-antibody complexes. In our previous study, pairwise decomposition of residue interaction energies of single chain Fv with HIV-1 p17 epitope variants has indicated the key specific residues in the complementary determining regions (CDRs) of scFv anti-p17. In this present investigation in order to determine whether a specific side chain group of residue in CDRs plays an important role in bioactivity, computational alanine scanning has been applied. Molecular dynamics simulations were done with several complexes of original scFv anti-p17 and scFv anti-p17mutants with HIV-1 p17 epitope variants with a production run up to 10 ns. With the combination of pairwise decomposition residue interaction and alanine scanning calculations, the point mutation has been initially selected at the position MET100 to improve the residue binding affinity. The calculated docking interaction energy between a single mutation from methionine to either arginine or glycine has shown the improved binding affinity, contributed from the electrostatic interaction with the negative favorably interaction energy, compared to the wild type. Theoretical calculations agreed well with the results from the peptide ELISA results. Panthip Tue-ngeun, Kanchanok Kodchakorn, Piyarat Nimmanpipug, Narin Lawan, Sawitree Nangola, Chatchai Tayapiwatana, Noorsaadah Abdul Rahman, Sharifuddin Md. Zain, and Vannajan Sanghiran Lee Copyright © 2013 Panthip Tue-ngeun et al. All rights reserved. A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics Thu, 07 Nov 2013 11:56:03 +0000 We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via 1H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two. Roman Eisner, Russell Greiner, Victor Tso, Haili Wang, and Richard N. Fedorak Copyright © 2013 Roman Eisner et al. All rights reserved. A Neural Network Model Can Explain Ventriloquism Aftereffect and Its Generalization across Sound Frequencies Mon, 21 Oct 2013 10:31:36 +0000 Exposure to synchronous but spatially disparate auditory and visual stimuli produces a perceptual shift of sound location towards the visual stimulus (ventriloquism effect). After adaptation to a ventriloquism situation, enduring sound shift is observed in the absence of the visual stimulus (ventriloquism aftereffect). Experimental studies report opposing results as to aftereffect generalization across sound frequencies varying from aftereffect being confined to the frequency used during adaptation to aftereffect generalizing across some octaves. Here, we present an extension of a model of visual-auditory interaction we previously developed. The new model is able to simulate the ventriloquism effect and, via Hebbian learning rules, the ventriloquism aftereffect and can be used to investigate aftereffect generalization across frequencies. The model includes auditory neurons coding both for the spatial and spectral features of the auditory stimuli and mimicking properties of biological auditory neurons. The model suggests that different extent of aftereffect generalization across frequencies can be obtained by changing the intensity of the auditory stimulus that induces different amounts of activation in the auditory layer. The model provides a coherent theoretical framework to explain the apparently contradictory results found in the literature. Model mechanisms and hypotheses are discussed in relation to neurophysiological and psychophysical data. Elisa Magosso, Filippo Cona, and Mauro Ursino Copyright © 2013 Elisa Magosso et al. All rights reserved. Modelling HIV/AIDS Epidemic among Men Who Have Sex with Men in China Mon, 30 Sep 2013 12:03:19 +0000 A compartmental model with antiviral therapy was proposed to identify the important factors that influence HIV infection among gay men in China and suggest some effective control strategies. We proved that the disease will be eradicated if the reproduction number is less than one. Based on the number of annual reported HIV/AIDS among MSM we used the Markov-Chain Monte-Carlo (MCMC) simulation to estimate the unknown parameters. We estimated a mean reproduction number of 3.88 (95% CI: 3.69–4.07). The estimation results showed that there were a higher transmission rate and a lower diagnose rate among MSM than those for another high-risk population. We compared the current treatment policy and immediate therapy once people are diagnosed with HIV, and numerical studies indicated that immediate antiviral therapy would lead to few HIV new infections conditional upon relatively low infectiousness; otherwise the current treatment policy would result in low HIV new infection. Further, increasing treatment coverage rate may lead to decline in HIV new infections and be beneficial to disease control, depending on the infectiousness of the infected individuals with antiviral therapy. The finding suggested that treatment efficacy (directly affecting infectiousness), behavior changes, and interventions greatly affect HIV new infection; strengthening intensity will contribute to the disease control. Xiaodan Sun, Yanni Xiao, Zhihang Peng, and Ning Wang Copyright © 2013 Xiaodan Sun et al. All rights reserved. Investigation of Nonlinear Pupil Dynamics by Recurrence Quantification Analysis Thu, 26 Sep 2013 15:15:17 +0000 Pupil is controlled by the autonomous nervous system (ANS). It shows complex movements and changes of size even in conditions of constant stimulation. The possibility of extracting information on ANS by processing data recorded during a short experiment using a low cost system for pupil investigation is studied. Moreover, the significance of nonlinear information contained in the pupillogram is investigated. We examined 13 healthy subjects in different stationary conditions, considering habitual dental occlusion (HDO) as a weak stimulation of the ANS with respect to the maintenance of the rest position (RP) of the jaw. Images of pupil captured by infrared cameras were processed to estimate position and size on each frame. From such time series, we extracted linear indexes (e.g., average size, average displacement, and spectral parameters) and nonlinear information using recurrence quantification analysis (RQA). Data were classified using multilayer perceptrons and support vector machines trained using different sets of input indexes: the best performance in classification was obtained including nonlinear indexes in the input features. These results indicate that RQA nonlinear indexes provide additional information on pupil dynamics with respect to linear descriptors, allowing the discrimination of even a slight stimulation of the ANS. Their use in the investigation of pathology is suggested. L. Mesin, A. Monaco, and R. Cattaneo Copyright © 2013 L. Mesin et al. All rights reserved. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting Tue, 24 Sep 2013 11:16:09 +0000 Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%). Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings. Andrew D. Revell, Gerardo Alvarez-Uria, Dechao Wang, Anton Pozniak, Julio S. Montaner, H. Clifford Lane, and Brendan A. Larder Copyright © 2013 Andrew D. Revell et al. All rights reserved. Computational Analysis of the Soluble Form of the Intracellular Chloride Ion Channel Protein CLIC1 Sun, 08 Sep 2013 15:55:34 +0000 The chloride intracellular channel (CLIC) family of proteins has the remarkable property of maintaining both a soluble form and an integral membrane form acting as an ion channel. The soluble form is structurally related to the glutathione-S-transferase family, and CLIC can covalently bind glutathione via an active site cysteine. We report approximately 0.6 s of molecular dynamics simulations, encompassing the three possible ligand-bound states of CLIC1, using the structure of GSH-bound human CLIC1. Noncovalently bound GSH was rapidly released from the protein, whereas the covalently ligand-bound protein remained close to the starting structure over 0.25 s of simulation. In the unliganded state, conformational changes in the vicinity of the glutathione-binding site resulted in reduced reactivity of the active site thiol. Elastic network analysis indicated that the changes in the unliganded state are intrinsic to the protein architecture and likely represent functional transitions. Overall, our results are consistent with a model of CLIC function in which covalent binding of glutathione does not occur spontaneously but requires interaction with another protein to stabilise the GSH binding site and/or transfer of the ligand. The results do not indicate how CLIC1 undergoes a radical conformational change to form a transmembrane chloride channel but further elucidate the mechanism by which CLICs are redox controlled. Peter M. Jones, Paul M. G. Curmi, Stella M. Valenzuela, and Anthony M. George Copyright © 2013 Peter M. Jones et al. All rights reserved. Pharmacophore Modeling and Docking Studies on Some Nonpeptide-Based Caspase-3 Inhibitors Sun, 08 Sep 2013 15:40:34 +0000 Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH3), and two acceptor (A) groups (–C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (; pred_). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target. Simant Sharma, Arijit Basu, and R. K. Agrawal Copyright © 2013 Simant Sharma et al. All rights reserved. A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome Tue, 03 Sep 2013 14:16:38 +0000 The diagnosis of carpal tunnel syndrome, a peripheral nerve disorder, at the earliest possible stage is very crucial because if left untreated it may cause permanent nerve damage reducing the chances of successful treatment. Here a novel Fuzzy Expert System designed using MATLAB is proposed for identification of severity of CTS. The data used were the nerve conduction study data obtained from Kannur Medical College, India. It consists of thirteen input fields, which include the clinical values of the diagnostic test and the clinical symptoms, and the output field gives the disease severity. The results obtained match with the expert’s opinion with 98.4% accuracy and high degrees of sensitivity and specificity. Since quantification of the intensity of CTS is a crucial step in the electrodiagnostic procedure and is important for defining prognosis and therapeutic measures, such an expert system can be of immense use in those regions where the service of such specialists may not be readily available. It may also prove useful in combination with other systems in providing diagnostic and predictive medical opinions and can add value if introduced into the routine clinical consultations to arrive at the most accurate medical diagnosis in a timely manner. Reeda Kunhimangalam, Sujith Ovallath, and Paul K. Joseph Copyright © 2013 Reeda Kunhimangalam et al. All rights reserved. An Alternative Approach to Protein Folding Mon, 02 Sep 2013 14:02:41 +0000 A diffusion theory-based, all-physical ab initio protein folding simulation is described and applied. The model is based upon the drift and diffusion of protein substructures relative to one another in the multiple energy fields present. Without templates or statistical inputs, the simulations were run at physiologic and ambient temperatures (including pH). Around 100 protein secondary structures were surveyed, and twenty tertiary structures were determined. Greater than 70% of the secondary core structures with over 80% alpha helices were correctly identified on protein ranging from 30 to 200 amino-acid sequence. The drift-diffusion model predicted tertiary structures with RMSD values in the 3–5 Angstroms range for proteins ranging 30 to 150 amino acids. These predictions are among the best for an all ab initio protein simulation. Simulations could be run entirely on a desktop computer in minutes; however, more accurate tertiary structures were obtained using molecular dynamic energy relaxation. The drift-diffusion model generated realistic energy versus time traces. Rapid secondary structures followed by a slow compacting towards lower energy tertiary structures occurred after an initial incubation period in agreement with observations. Yeona Kang and Charles M. Fortmann Copyright © 2013 Yeona Kang and Charles M. Fortmann. All rights reserved. Computational Elucidation of Structural Basis for Ligand Binding with Leishmania donovani Adenosine Kinase Wed, 24 Jul 2013 11:50:22 +0000 Enzyme adenosine kinase is responsible for phosphorylation of adenosine to AMP and is crucial for parasites which are purine auxotrophs. The present study describes development of robust homology model of Leishmania donovani adenosine kinase to forecast interaction phenomenon with inhibitory molecules using structure-based drug designing strategy. Docking calculation using reported organic small molecules and natural products revealed key active site residues such as Arg131 and Asp16 for ligand binding, which is consistent with previous studies. Molecular dynamics simulation of ligand protein complex revealed the importance of hydrogen bonding with active site residues and solvent molecules, which may be crucial for successful development of drug candidates. Precise role of Phe168 residue in the active site was elucidated in this report that provided stability to ligand-protein complex via aromatic-π contacts. Overall, the present study is believed to provide valuable information to design a new compound with improved activity for antileishmanial therapeutics development. Rajiv K. Kar, Md. Yousuf Ansari, Priyanka Suryadevara, Bikash R. Sahoo, Ganesh C. Sahoo, Manas R. Dikhit, and Pradeep Das Copyright © 2013 Rajiv K. Kar et al. All rights reserved. Elucidation of Novel Structural Scaffold in Rohu TLR2 and Its Binding Site Analysis with Peptidoglycan, Lipoteichoic Acid and Zymosan Ligands, and Downstream MyD88 Adaptor Protein Mon, 15 Jul 2013 13:31:20 +0000 Toll-like receptors (TLRs) play key roles in sensing wide array of microbial signatures and induction of innate immunity. TLR2 in fish resembles higher eukaryotes by sensing peptidoglycan (PGN) and lipoteichoic acid (LTA) of bacterial cell wall and zymosan of yeasts. However, in fish TLR2, no study yet describes the ligand binding motifs in the leucine rich repeat regions (LRRs) of the extracellular domain (ECD) and important amino acids in TLR2-TIR (toll/interleukin-1 receptor) domain that could be engaged in transmitting downstream signaling. We predicted these in a commercially important freshwater fish species rohu (Labeo rohita) by constructing 3D models of TLR2-ECD, TLR2-TIR, and MyD88-TIR by comparative modeling followed by 40 ns (nanosecond) molecular dynamics simulation (MDS) for TLR2-ECD and 20 ns MDS for TLR2-TIR and MyD88-TIR. Protein (TLR2-ECD)–ligands (PGN, LTA, and zymosan) docking in rohu by AutoDock4.0, FlexX2.1, and GOLD4.1 anticipated LRR16–19, LRR12–14, and LRR20-CT as the most important ligand binding motifs. Protein (TLR2-TIR)—protein (MyD88-TIR) interaction by HADDOCK and ZDOCK predicted BB loop, αB-helix, αC-helix, and CD loop in TLR2-TIR and BB loop, αB-helix, and CD loop in MyD88-TIR as the critical binding domains. This study provides ligands recognition and downstream signaling. Bikash Ranjan Sahoo, Madhubanti Basu, Banikalyan Swain, Manas Ranjan Dikhit, Pallipuram Jayasankar, and Mrinal Samanta Copyright © 2013 Bikash Ranjan Sahoo et al. All rights reserved. Hyperpolarization-Activated Current, , in Mathematical Models of Rabbit Sinoatrial Node Pacemaker Cells Mon, 08 Jul 2013 15:01:07 +0000 A typical feature of sinoatrial (SA) node pacemaker cells is the presence of an ionic current that activates upon hyperpolarization. The role of this hyperpolarization-activated current, , which is also known as the “funny current” or “pacemaker current,” in the spontaneous pacemaker activity of SA nodal cells remains a matter of intense debate. Whereas some conclude that plays a fundamental role in the generation of pacemaker activity and its rate control, others conclude that the role of is limited to a modest contribution to rate control. The ongoing debate is often accompanied with arguments from computer simulations, either to support one's personal view or to invalidate that of the antagonist. In the present paper, we review the various mathematical descriptions of that have been used in computer simulations and compare their strikingly different characteristics with our experimental data. We identify caveats and propose a novel model for based on our experimental data. Arie O. Verkerk and Ronald Wilders Copyright © 2013 Arie O. Verkerk and Ronald Wilders. All rights reserved. Transmission Model of Hepatitis B Virus with the Migration Effect Mon, 24 Jun 2013 08:48:09 +0000 Hepatitis B is a globally infectious disease. Mathematical modeling of HBV transmission is an interesting research area. In this paper, we present characteristics of HBV virus transmission in the form of a mathematical model. We analyzed the effect of immigrants in the model to study the effect of immigrants for the host population. We added the following flow parameters: “the transmission between migrated and exposed class” and “the transmission between migrated and acute class.” With these new features, we obtained a compartment model of six differential equations. First, we find the basic threshold quantity Ro and then find the local asymptotic stability of disease-free equilibrium and endemic equilibrium. Furthermore, we find the global stability of the disease-free and endemic equilibria. Previous similar publications have not added the kind of information about the numerical results of the model. In our case, from numerical simulation, a detailed discussion of the parameters and their numerical results is presented. We claim that with these assumptions and by adding the migrated class, the model informs policy for governments, to be aware of the immigrants and subject them to tests about the disease status. Immigrants for short visits and students should be subjected to tests to reduce the number of immigrants with disease. Muhammad Altaf Khan, Saeed Islam, Muhammad Arif, and Zahoor ul Haq Copyright © 2013 Muhammad Altaf Khan et al. All rights reserved. Global Stability of Vector-Host Disease with Variable Population Size Sun, 09 Jun 2013 15:25:18 +0000 The paper presents the vector-host disease with a variability in population. We assume, the disease is fatal and for some cases the infected individuals become susceptible. We first show the local and global stability of the disease-free equilibrium, for the case when . We also show that for , the disease free-equilibrium of the model is both locally as well as globally stable. For , there exists a unique positive endemic equilibrium. For , the disease persistence occurs. The endemic equilibrium is locally as well as globally asymptotically stable for . Numerical results are presented for the justifications of theoratical results. Muhammad Altaf Khan, Saeed Islam, Sher Afzal Khan, and Gul Zaman Copyright © 2013 Muhammad Altaf Khan et al. All rights reserved.