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Call For Paper
Bentham Science Publishers would like to invite you to submit your research paper for publishing in the Journal of
Wednesday, November 2, 2016
Highlighted Article: Network Analysis of Protein Structures: The Comparison of Three Topologies
Network Analysis of Protein Structures: The Comparison of Three Topologies
Author(s):
Wenying Yan, Guang Hu and Bairong Shen Pages 480 - 489 ( 10 )
Abstract:
Topology plays a central role in the structure of a protein. Network theoretical methods are being increasingly applied to investigate protein topology. In this paper, amino acid contact energy networks (AACENs) are constructed for globular, transmembrane and toroidal proteins. The effects of topology on proteins are investigated by the differences of various network parameters among three kinds of protein topologies. Globular proteins are found to have the highest network density, average closeness and system vulnerability, while toroidal proteins have the lowest values of these parameters. Transmembrane proteins are found to have significantly higher assortativity values than globular and toroidal proteins. AACENs are constructed and compared for proteins with different secondary structure compositions, whose influences on biological functions are discussed in terms of topological descriptors. By extracting sub-networks only including interfacial residues between different chains, it may provide a simple but straightforward method to identify hot spots of toroidal proteins. This network study would offer new insight into overall topology and structural organization of different types of proteins.
Keywords:
Amino acid network, contact energy, protein topology, secondary structure, symmetry, toroidal proteins.
Affiliation:
Center for Systems Biology, Soochow University, Suzhou 215006, China.
Graphical Abstract:
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Tuesday, October 25, 2016
Most Accessed Article: Network Analysis of Protein Structures: The Comparison of Three Topologies
Network Analysis of Protein
Structures: The Comparison of Three Topologies
Author(s):
Wenying Yan, Guang Hu and Bairong ShenPages 480-489 (10)
Abstract:
Topology plays a central role in the structure of a protein. Network theoretical methods are being increasingly applied to investigate protein topology. In this paper, amino acid contact energy networks (AACENs) are constructed for globular, transmembrane and toroidal proteins. The effects of topology on proteins are investigated by the differences of various network parameters among three kinds of protein topologies. Globular proteins are found to have the highest network density, average closeness and system vulnerability, while toroidal proteins have the lowest values of these parameters. Transmembrane proteins are found to have significantly higher assortativity values than globular and toroidal proteins. AACENs are constructed and compared for proteins with different secondary structure compositions, whose influences on biological functions are discussed in terms of topological descriptors. By extracting sub-networks only including interfacial residues between different chains, it may provide a simple but straightforward method to identify hot spots of toroidal proteins. This network study would offer new insight into overall topology and structural organization of different types of proteins.
Keywords:
Amino acid network, contact energy, protein topology, secondary
structure, symmetry, toroidal proteins.
Affiliation:
Center for Systems Biology, Soochow University, Suzhou 215006,
China.
Graphical
Abstract:
For More Information please Visit Our Website Current Bioinformatics
Wednesday, October 19, 2016
Podcast on A New Binding Site Involving the C-terminal Domain to Design Specific Inhibitors of PepX
Podcast on A New Binding Site Involving the C-terminal Domain to Design Specific Inhibitors of PepX
Wednesday, October 5, 2016
Major Article Contributions by Some of our Chinese Authors in Bentham Science Publishers Journal; Current Bioinformatics
Article Title:
Integrative Approaches for microRNA Target Prediction: Combining Sequence Information and the Paired mRNA and miRNA Expression Profiles
Author(s): Naifang Su, Minping Qian and Minghua Deng
Abstract:
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and could improve our comprehensive understanding of gene regulation.
For more information visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106265
Article Title: SubChlo-GO: Predicting Protein Subchloroplast Locations with Weighted Gene Ontology Scores
Author(s): Pufeng Du, Tingting Li, Xin Wang and Chao Xu
Abstract:
Chloroplasts are subcellular organelles found only in green plants and eukaryotic algae. Chloroplasts are of central importance in the photosynthesis process. The subchloroplast localizations of chloroplast proteins are critical in understanding their functions and important for fully decipher the photosynthesis process. Although there are several existing methods that computationally determine protein subchloroplast localizations, prediction performance and software availability can still be improved. We proposed a novel computational method, namely, the Weighted Gene Ontology Scores, to predict protein subchloroplast locations. This method can achieve at least 88% prediction accuracy on the benchmarking dataset, which is significantly higher than existing methods. SubChlo-GO, which is an easy-to-use webbased online service, has been constructed based on the proposed method. We hope that SubChlo-GO could be helpful in chloroplast proteome research.
For more details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=107677
Article Title: Analysis of Gene Logic Networks for Arabidopsis
Author(s): Yansen Su, Shudong Wang, Eryan Li, Tao Song, Hui Yu and Dazhi Meng
Abstract:
External stimuli may activate the stress response in Arabidopsis thaliana. The molecules which play important roles in the stress response have been widely studied. However, the interactions, especially logic interactions, among these molecules, need to be studied. In this paper, logic networks are constructed based on gene expression profiles of Arabidopsis under the normal condition and four different stimuli conditions, respectively. It is found that the distribution of different types of 2-order logics in the gene logic network under the normal condition is different from the others. Furthermore, the logic networks of genes which play important roles are constructed and their dynamics are simulated. It is then observed that the number of attractors in the logic network for Arabidopsis under the normal condition is less than those under four external stimuli. It is also observed that the number of attractors with large attraction domain in the logic network for Arabidopsis under the normal condition is greater than those under four external stimuli. The results show that the distribution of different types of 2-order logics and the number of attractors clearly distinguish logic network under the normal condition from those under external stimuli conditions. Our studies will provide the theoretical basis for experimental studies on the stress response of Arabidopsis.
For more details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=107682
Article Title: Periodic Correlation Structures in Bacterial and Archaeal Complete Genomes
Author(s): Zuo-Bing Wu
Abstract:
The periodic transference of nucleotide strings in bacterial and archaeal complete genomes is investigated by using the metric representation and the recurrence plot method. The generated periodic correlation structures exhibit four kinds of fundamental transferring characteristics: a single increasing period, several increasing periods, an increasing quasi-period and almost noincreasing period. The mechanism of the periodic transference is further analyzed by determining all long periodic nucleotide strings in the bacterial and archaeal complete genomes and is explained as follows: both the repetition of basic periodic nucleotide strings and the transference of non-periodic nucleotide strings would form the periodic correlation structures with approximately the same increasing periods.
For more details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=107685
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Phosphonate emerging zinc binding group in matrix metalloproteinase inhibitors
Phosphonate emerging zinc binding group in matrix metalloproteinase inhibitors
Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases, responsible for the degradation of the extracellular matrix (ECM). Their involvement has been demonstrated in several diseases, among which chronic inflammation and cancer; therefore, they have been considered interesting therapeutic targets. The design of MMP inhibitors (MMPIs) has largely focused on development of various compounds containing a zinc binding group (ZBG) in their structure, with the hydroxamate being the most potent one.
Currently, there are no MMPI containing hydroxamate function as ZBG in the market, mainly due to their proven toxicity. An alternative function chelating the zinc ion would ensure a better selectivity. This latter aspect is mandatory to obtain clinically exploitable compounds. In this review the phosphonate and bis-phosphonate groups as the ZBG are considered in the development of selective and potent MMPIs.
The authors have been working since a long time on MMP inhibitors containing alternative zinc-binding group to hydroxamate, focusing in particular on phosphonic acids, thus contributing to this still interesting research field.
Phosphonate-based inhibitors are classified on peptide phosphonate; sulphonamide and sulfonyl phosphonate, the most potent; carbamoyl phosphonate, and bisphosphonate that have been identified more recently with an interesting activity on bone resorption.
Moreover, the binding with the MMP active site is discussed in the research paper, ‘Phosphonate Emerging Zinc Binding Group in Matrix Metalloproteinase Inhibitors’, published in Current Drug Targets.
https://currentbioinformatics.wordpress.com/phosphonate-emerging-zinc-binding-group-in-matrix-metalloproteinase-inhibitors/
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Mind and molecules — Fingerprinting psychiatric illness
Mind and molecules Fingerprinting psychiatric illness
Diagnosis of mood and psychotic disorders depend solely on relatively subjective assessment of symptoms and psychometric evaluations, upon which a decision is made to prescribe one or more standardised treatment regimen. Treatment response in turn is evaluated on the same principles. All this in spite of decades’ worth of research efforts aimed at understanding the neurobiological underpinnings of these disorders.
Research into mood (depression, bipolar disorder) and psychotic disorders has advanced to the extent where biochemical hypotheses explaining the aetiology of a particular illness may be individualised to more accurately target one or more underlying pathology in a specific patient or subgroup of patients, hence achieving more effective disease modifying therapy. A “one-size fits all” paradigm is no longer a viable approach. Rather a customized regime based on individual biological abnormalities would pave the way toward more effective treatment.
In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders by exploring various biomarkers relating to neuroanatomy, neuro-circuitry, neuronal growth and resilience as well as markers associated with oxidative stress and inflammation. A brief overview of prominent markers in the fields of genetics and proteomics also offers additional insight. Scrutinizing prominent and more equivocal biological markers of mood and psychotic disorders aids to address the urgent need to identify neurobiological targets of a disease as well as its associated biomarkers that will improve the current classification, diagnosis and treatment of these disorders. Ultimately, this knowledge will inform on the development of biomarker panels that in turn will customize treatment regimens for better therapeutic outcomes. The identified biomarkers should accurately reflect pathophysiologic processes in these disorders that will enable practitioners to stratify patients on a biological basis into more homogeneous clinically distinct subgroups, allowing the prescribing of target-specific therapy.
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Recently Published Issue of the Journal Current Bioinformatics
Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science.
Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
Following are the articles from the Journal of Current Bioinformatics, 10 Issue 2:
Article: Investigating Power and Limitations of Ensemble Motif Finders Using Metapredictor CE3
Author(s): Mauro Leoncini, Manuela Montagnero and Karina Panucia Tillán
Article: Protein Sequence Annotation by Means of Community Detection
Author(s): Giuseppe Profiti, Damiano Piovesan, Pier Luigi Martelli, Piero Fariselli and Rita Casadio
Article: Discretization of Expression Quantitative Trait Loci in Association Analysis Between Genotypes and Expression Data§
Author(s): Andrés R. Masegosa, Rubén Armañanzas, María M. Abad-Grau, Víctor Potenciano, Serafín Moral, Pedro Larrañaga, Concha Bielza and Fuencisla Matesanz
Article: Lexical Characterisation of Bio-Ontologies by the Inspection of Regularities in Labels
Author(s): Manuel Quesada-Martínez, Jesualdo Tomás Fernández-Breis and Robert Stevens
Article: Incremental Construction of Biological Networks by Relation Extraction from Literature
Author(s): Dragana Miljkovic, Vid Podpečan, Tjaša Stare, Igor Mozetič, Kristina Gruden and Nada Lavrač
Article: Efficient and Error-Tolerant Sequencing Read Mapping
Author(s): Piotr Jaroszyński and Norbert Dojer
Article: A Hierarchical Classification for the Selection of the Most Suitable Multiple Sequence Alignment Methodology
Author(s): Francisco M. Ortuño, Hector Pomares, Olga Valenzuela, Carolina Torres and Ignacio Rojas
Article: Regulation of Meiosis Initiation before the Commitment Point in Budding Yeast: A Review of Biology, Molecular Mechanisms and Related Mathematical Models
Author(s): Clampi T. Wannige, Don Kulasiri and Sandhya Samarasinghe
Article: PuzzleCluster: A Novel Unsupervised Clustering Algorithm for Binning DNA Fragments in Metagenomics
Author(s): Kyler Siegel, Kristen Altenburger, Yu-Sing Hon, Jessey Lin and Chenglong Yu
Article: Effect of Hubs in Amino Acid Network on Iron Superoxide Dismutase Stability
Author(s): Yanrui Ding, Xueqin Wang and Zhaolin Mou
For details, please visit: http://bit.ly/1dJDm9V
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Major Article Contributions by some of the Indian Authors in Bentham Science Publishers Journal; Current Bioinformatics:
Article Title: A Computational Prediction of Conserved MicroRNA Targets of Ion Channels in Vertebrates
Author(s): Priyadarshan Kathirvel, Gopal Ramesh Kumar and Kavitha
Sankaranarayanan Abstract: Ion channels are integral membrane proteins that are responsible for most physiological functions including the electrical activity of excitable cells. Their expression and function are regulated by a variety of means including microRNAs (miRNAs). MicroRNAs are small non coding RNAs, 22 nucleotides in length, involved in regulation of gene expression. In this study, we attempt to predict the miRNA targets of ion channel genes conserved across 4 species using TargetScan algorithm. From the results, many miRNA targets were found to be conserved among mammals. The expression profile of identified miRNA targets of certain genes that are implicated in channelopathies of brain, heart and skeletal muscle in Homo sapiens are explored. Further a final correlation of channel genes and miRNAs expressed in specific tissues is obtained by comparison of expression profiles using mimiRNA. miR-302a and miR-9 were found to be positively correlated with many channelopathy associated genes, while miR-143 and miR-29a were predicted to exhibit negative correlation. In this paper we summarize a list of all possible miRNAs linked with channelopathies. For details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106272
Article Title: Mangrove Infoline Database: A Database of Mangrove Plants with Protein Sequence Information Author(s): Sambhaji B. Thakar and Kailas D. Sonawane Abstract: Mangrove Infoline Database contains information about the medicinal uses of mangrove plants with protein/enzyme sequences. Mangrove Infoline Database is a comprehensive dynamic web-based database, used to facilitate retrieval of information related to the mangrove medicinal species. Most of the enzyme sequences extracted from mangroves are taken from NCBI’s protein sequence database, whereas the information related to physical characteristics, geographical distribution, common/vernacular names, taxonomy IDs, medicinal uses, parts used, chemical components extracted from various mangrove plants which can be used as a drug molecules have been collected from various literatures and scientific journals available in the text form. NCBI’s BLAST link has also been provided for the further comparative study. So there was a need to build database where users could get all specific information about mangrove medicinal plants at one place. The current database contains information about 100 Mangrove medicinal species out of which 40 are True mangroves, 30 Minor mangroves and, 30 Associate mangroves. This database would be useful to explore mangrove medicinal plant information through Web based database which would be helpful to derive the information for Researchers, Scientists, Pharmacologists, Biologists, Chemists, Doctors/Pharmacists, Teaching (Students and Teachers from universities and schools), Home-User, Botanical interested Persons, Farmers, and finally mangrove lovers. This attempt would create social awareness among the users about the important applications, uses and conservation of mangrove species around the world. For more information, kindly visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114022
Article Title: Credential Role of van der Waal Volumes and Atomic Masses in Modeling Hepatitis C Virus NS5B Polymerase Inhibition by Tetrahydrobenzo- Thiophenes Using SVM and MLR Aided QSAR Studies Author(s): Kirti Khuntwal, Mukesh Yadav, Anuraj Nayarisseri, Shobha Joshi, Deepika Sharma and Smita Suhane Abstract: Chronic hepatitis C virus (HCV) infections are a significant health problem worldwide. The NS5B Polymerase of HCV plays a central role in virus replication and is a prime target for the discovery of new treatment options. The urgent need to develop novel anti-HCV agents has provided an impetus for understanding the structure-activity relationship of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Towards this objective, multiple linear regression (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models for a dataset of 34 Tetrahydrobenzothiophene derivatives. The statistical analysis showed that the models derived from both SVM (R2 = 0.9784, SE=0.2982, R2 cv = 0.92) and MLR (R2=0.9684, SE=0.1171, R2 cv= 0.955) have a good internal predictivity. The models were also validated using external test set validation and Y-scrambling, the results demonstrated that MLR has a significant predictive ability for the external dataset as compared to SVM. Also the model is found to yield reliable clues for further optimization of Tetrahydrobenzothiophene derivatives in the data set. For more details, click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114016
Article Title: Development of an Engineering Method to Optimize Polyamine Metabolic Pathways. Author(s): Mouli Das, Subhasis Mukhopadhyay and Rajat K. De Abstract: In this article, we determine an optimal set of enzymes which is needed to be expressed at a specific level, in order to maximize the production of the target metabolite, spermine, from the substrate, ornithine, in the polyamine metabolic pathway. The pathway thus obtained is compared with the optimal pathway obtained using the existing extreme pathway analysis method. The results are appropriately validated through literature. Finally, we discuss potential applications of this approach in the field of metabolic and genetic engineering. It is hoped that the engineering of this pathway can produce secondary metabolites having commercial values, and finally drugs. For more details click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116191
Article Title: PredictFold-PSS-3D1D: A Protein Fold Recognition Server for Predicting Folds from the Twilight Zone Sequences. Author(s): Kaliappan Ganesan and Subbiah Parthasarathy Abstract: The PredictFold-PSS-3D1D is an online protein fold recognition web server used to predict the possible folds from the twilight zone protein sequences. In this server, an improved 3D1D profile method (Ganesan and Parthasarathy, J. Struct. Funct. Genomics, 12, 181-189, 2011) is employed, wherein, the inclusion of predicted secondary structure information improves fold recognition. The PredictFold-PSS-3D1D server accepts amino acid sequences and their predicted secondary structure data as input and aligns them with the 3D1D profiles of known SCOP folds in a database. The alignments are ranked by the z-values and P-values. The top 5 ranks of the SCOP folds from the database are listed along with a link to ‘View SCOP details’. The folds with z-values ≥3.0 and P-values ≤0.05 are indicated as ‘Predicted Fold’ for the given query twilight zone protein sequence. This server is available in our PredictFold web server athttp://bioinfo.bdu.ac.in/pss3d1d/. For more information, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116188
Article Title: An In Silico Identification of Human Promoters: A Soft Computing Based Approach Author(s): Sutapa Datta and Subhasis Mukhopadhyay Abstract: Promoter region of a gene sequence of Eukaryotes is very important as it helps us to understand the mechanism of transcription regulation. The identification of this region is a complex problem as the signature for identification turns out to be fuzzy. Several in silico methods are available for identifying the promoter region, but the scope for new methods still exists. Reasonable prediction of promoter sequence (that can be tested by comparing with the wet-lab data) from a mixed database of promoters and nonpromoters is thus a challenge that any new method would have to face. In this communication we propose a composite method that utilizes clustering of known promoter and non-promoter sequences in their respective clusters based on their relative distances, and then classifying the max similarity scores obtained from a group of new sequences and the clusters, to predict the true promoters among the new set of sequences. The in silico experiment is carried out on different databases constructed by us from the available primary sequence databanks to demonstrate the advantage of the proposed approach. For more information on this journal, click:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=111148
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Japanese authors have made important contributions to Bentham Science Journal, Current Bioinformatics
Journal Name:Current Bioinformatics
Article Title: Signal-Dependent Noise Induces Muscle Co-Contraction to Achieve Required Movement Accuracy: A Simulation Study with an Optimal Control
Article Title: Signal-Dependent Noise Induces Muscle Co-Contraction to Achieve Required Movement Accuracy: A Simulation Study with an Optimal Control
Author(s): Yuki Ueyama and Eizo Miyashita
Abstract
Simultaneous activation of the agonist and antagonist muscles surrounding a joint, called co-contraction, is suggested to play a role in increasing joint stiffness to improve movement accuracy. However, it has not been clarified how co-contraction is related to movement accuracy, as most models for motor planning and control cannot deal with muscle co-contraction. In this study, the muscle activation and joint stiffness in reaching movements were studied under three different requirement levels of endpoint accuracy using a two-joint six-muscle model and an approximately optimal control. We carried out simulations of biological arm movements for a center-out reaching task under different accuracy demands with different types of motor noise and demonstrated time-varying co-contraction and a double-peaked jointstiffness profile. Furthermore, we showed that the strength of co-contraction and joint stiffness increased depending on the required accuracy level under signal-dependent noise, the magnitude of which was proportional to the motor command but not to additive Gaussian noise. We concluded that the optimal control is a valid model for the human motor control system and that signal-dependent noise is essential to induce co-contraction depending on accuracy demands.
For more information, visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106262
Simultaneous activation of the agonist and antagonist muscles surrounding a joint, called co-contraction, is suggested to play a role in increasing joint stiffness to improve movement accuracy. However, it has not been clarified how co-contraction is related to movement accuracy, as most models for motor planning and control cannot deal with muscle co-contraction. In this study, the muscle activation and joint stiffness in reaching movements were studied under three different requirement levels of endpoint accuracy using a two-joint six-muscle model and an approximately optimal control. We carried out simulations of biological arm movements for a center-out reaching task under different accuracy demands with different types of motor noise and demonstrated time-varying co-contraction and a double-peaked jointstiffness profile. Furthermore, we showed that the strength of co-contraction and joint stiffness increased depending on the required accuracy level under signal-dependent noise, the magnitude of which was proportional to the motor command but not to additive Gaussian noise. We concluded that the optimal control is a valid model for the human motor control system and that signal-dependent noise is essential to induce co-contraction depending on accuracy demands.
For more information, visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106262
courtesy by :Bentham Insight
Saturday, July 2, 2016
On the Discovery of Cellular Subsystems in Gene Correlation Networks Using Measures of Centrality
Author(s):
Kathryn M. Dempsey and Hesham H. AliPages 305-314 (10)
Abstract:
Innovative models for analyzing high-throughput biological data are becoming of great significance in the post genomic era. Correlation networks are rapidly becoming powerful models for representing various types of biological relationships especially in the case of extracting knowledge from gene expression data. Data analysis using of other popular networks models in biology have revealed that structures within a graph model, such as high degee nodes and cliques, often correspond to cellular functions. Correlation networks, which can be used to measure the relationships between patterns of gene expression, are capable of representing entire-genome expression assays. In this study we build correlation networks from gene expression datasets available in the public domain; once built, we are able to identify graph theoretic structures (critical nodes and dense subgraphs) and use measures of centrality to infer the biological impact of these structures within the network. We go on to validate the link between network components (such as critical nodes and degrees) and biological function of the model by exploring the biological properties of a set of nodes with high centrality measures in the correlation. In addition, we use network integration to identify essential genes in an integrated correlation network obtained by the union of networks of mice with different age groups. By examining clusters connected by highly central nodes in this integrated network, we were able to find a set of essential genes and identify several cellular subsystems that point towards aging related mechanisms. The obtained results provide clear evidence that correlation networks represent a powerful tool for analyzing temporal biological data and consequently make use of the wealth of gene expression assays currently available.
Keywords:
Celluar subsystems, centrality measures, correlation networks, essential genes, graph parameters.
Affiliation:
College of Information Science & Technology, University of Nebraska at Omaha, 6001 Dodge St., PKI 172 Omaha, NE 68182, USA.
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Review of Stochastic Stability and Analysis Tumor-Immune Systems
Author(s):
Chi s Oana, Opri s Dumitru and Concu RiccardoPages 390-400 (11)
Abstract:
In this paper we review and at the same time investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability around the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function and the second order moments. We have studied and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. These stochastic models are studied from stability point of view and they were graphically represented using the second order Euler scheme and Maple 12 software.
Keywords:
Lyapunov exponent, lyapunov function, stochastic models, stochastic stability, wiener process.
Affiliation:
Instituto de Investigaciones Marinas, Spanish Council for Scientific Research (IIM-CSIC), C/Eduardo Cabello 6, Vigo, Spain.
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S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal Sciences
Author(s):
Cristian R. Munteanu, Alexandre L. Magalhaes, Aliuska Duardo-Sanchez, Alejandro Pazos and Humberto Gonzalez-DiazPages 429-437 (9)
Abstract:
The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This allows the numerical quantification of the significant information contained by the sequences of amino acids, nucleotides or types of tax laws. In this paper we describe S2SNet, a new Python tool with a graphical user interface that can transform any sequence of characters or numbers into series of invariant star network topological indices. The application is based on Python reusable processing procedures that perform different functions such as reading sequence data, transforming numerical series into character sequences, changing letter codification of strings and drawing the star networks of each sequence using Graphviz package as graphical back-end. S2SNet was previously used to obtain classification models for natural/random proteins, breast/colon/prostate cancer-related proteins, DNA sequences of mycobacterial promoters and for early detection of diseases and drug-induced toxicities using the blood serum proteome mass spectrum. In order to show the extended practical potential of S2SNet, this work presents several examples of application for proteins, DNA/RNA, blood proteome mass spectra and time evolution of the financial law recurrence. The obtained topological indices can be used to characterize systems by creating classification models, clustering or pattern search with statistical, Neural Network or Machine Learning methods. The free availability of S2SNet, the flexibility of analyzing diverse systems and the Python portability make it an ideal tool in fields such as Bioinformatics, Proteomics, Genomics, and Biomedicine or Social, Economic and Political Sciences.
Keywords:
Complex network, financial law network, graph indices, interaction, network, protein, python application, social network.
Affiliation:
Department of Information and Communications Technologies, Computer Science Faculty, University of A Coruna, Campus de Elvina s/n, 15071 A Coruna, Spain.
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Similarity/Dissimilarity Analysis of Protein Sequences by a New Graphical Representation
Author(s):
Guohua Huang and Jerry HuPages 539-544 (6)
Abstract:
Mainly based on pKa (NH3+) values of amino acid, a novel graphical method without degeneracy for protein sequences has been proposed firstly, which assists in viewing, aligning and comparing multiple sequences visually. Then, a new algorithm to extract a 40-dimensional numerical vector from graphical curves has been presented to characterize protein sequences. The similar relationship among sequences is computed by Euclidean distance on corresponding numerical vectors. Finally, our method is applied for similarity analysis of protein sequences on two data sets. The results are in agreement with the acknowledged view proved by a great deal of evidence from anatomy and hence demonstrate the validity of this approach.
Keywords:
Graphical representation, numerical characterization, phylogenetic tree, protein sequence, similarity analysis.
Affiliation:
Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China.
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Recent Advances in Mathematical Modeling and Simulation of DNA Replication Process
Author(s):
Guoli Ji, Yong Zeng, Jinting Guan, Qingshun Q. Li, Congting Ye and Yunlong LiuPages 591-602 (12)
Abstract:
DNA replication is the basis for biological inheritance, involving a series of sophisticated biochemical processes. Over the past decades, numerous in vitro or in vivo experiments have been implemented among a variety of organisms. While these resource-intensive techniques may always be costly, time-consuming or unable to measure the replication process on a global scale. Recently, mathematical modeling and computational simulation of biochemical processes that can be used for rapid testing of biology hypotheses have attracted considerable attention. In this review, we outline some key mathematical and computational works proposed recently for DNA replication process, with emphasis on the modeling and simulation of the replication origin identification and characterization, the replication process initiation and regulation, and the genome-wide profiling of DNA replication. Although many excellent works have been done, for a deeper insight into the DNA replication process, further iteration of mathematical modeling and biochemical experiment are still needed, and the prospective and possible research directions are discussed herein.
Keywords:
Computational simulation, DNA replication, mathematical modeling, replication initiation, replication origin identification, replication regulation.
Affiliation:
Department of Automation, Xiamen University, Xiamen, Fujian, 361005, China.
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