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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