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ISSN 1386-6338 (P)
ISSN 1434-3207 (E)
In Silico Biology is a scientific research journal for the advancement of computational models and simulations applied to complex biological phenomena. We publish peer-reviewed leading-edge biological, biomedical and biotechnological research in which computer-based (i.e.,
) modeling and analysis tools are developed and utilized to predict and elucidate dynamics of biological systems, their design and control, and their evolution. Experimental support may also be provided to support the computational analyses.
In Silico Biology aims to advance the knowledge of the principles of organization of living systems. We strive to provide computational frameworks for understanding how observable biological properties arise from complex systems. In particular, we seek for integrative formalisms to decipher cross-talks underlying systems level properties, ultimate aim of multi-scale models.
Studies published in
In Silico Biology generally use theoretical models and computational analysis to gain quantitative insights into regulatory processes and networks, cell physiology and morphology, tissue dynamics and organ systems. Special areas of interest include signal transduction and information processing, gene expression and gene regulatory networks, metabolism, proliferation, differentiation and morphogenesis, among others, and the use of multi-scale modeling to connect molecular and cellular systems to the level of organisms and populations.
In Silico Biology also publishes foundational research in which novel algorithms are developed to facilitate modeling and simulations. Such research must demonstrate application to a concrete biological problem.
In Silico Biology frequently publishes special issues on seminal topics and trends. Special issues are handled by Special Issue Editors appointed by the Editor-in-Chief. Proposals for special issues should be sent to the Editor-in-Chief.
About In Silico Biology
is a pendant to
(in the living system) and
(in the test tube) biological experiments, and implies the gain of insights by computer-based simulations and model analyses.
In Silico Biology (ISB) was founded in 1998 as a purely online journal. IOS Press became the publisher of the printed journal shortly after. Today, ISB is dedicated exclusively to biological systems modeling and multi-scale simulations and is published solely by IOS Press. The previous online publisher, Bioinformation Systems, maintains a website containing studies published between 1998 and 2010 for archival purposes.
We strongly support open communications and encourage researchers to share results and preliminary data with the community. Therefore, results and preliminary data made public through conference presentations, conference proceeding or posting of unrefereed manuscripts on preprint servers will not prohibit publication in ISB. However, authors are required to modify a preprint to include the journal reference (including DOI), and a link to the published article on the ISB website upon publication.
Abstract: An in silico approach was used to investigate cytochrome c and the cytochrome c gene of Phanerochaete chrysosporium. The cytochrome c gene contains four introns. Omission of the introns reveals a DNA sequence coding for a complete predicted amino acid sequence for P. chrysosporium cytochrome c consistent with those of other cytochromes c. Fungal cytochromes c often have a short N-terminal peptide preceding a Gly that is the N-terminal amino acid in many cytochromes c. Thus…a microexon codes for an N-terminal pentapeptide (MetProTyrAlaPro) in P. chrysosporium that is identical to the N-terminal pentapeptide of Schizosaccharomyces pombe, a well studied yeast, the genome of which bears more similarity to higher eukaryotes than to other fungi. The fourth intron, when omitted, reveals the presence of another microexon resulting in a sequence for the C-terminal portion of the protein and the stop codon. Interestingly, two interpretations for the sequence of this intron leads to predictions that the C-terminal sequence ends with either AlaValAsn or AlaTyr. Selected aspects of the molecular architecture of cytochrome c and regulatory control elements of the P. chrysosporium cytochrome c gene were analyzed and compared to those present in other fungi and to those present in genes for lignin peroxidases and cytochromes P-450, two important families of hemeproteins produced by this fungus.
Abstract: The histone-like proteins (HU) belong to a family of DNA architectural proteins that stabilize nucleoprotein complexes. We found a putative HU protein (TgGlmHMM_3045) in Toxoplasma gondii genome that was homologous to the bacterial HU protein. This putative sequence was located in the scaffold TGG_995361 of the chromosome 10. The sequence included the prokaryotic bacterial histone-like domain, KFGSLGlRRRGERVARNPRT (ID number PS00045). HU protein sequences were also found in Plasmodium falciparum, Neospora caninum, Theileria…parva and Theileria annulata. We found that the homology of the putative HU protein in Apicomplexa was greater with bacterial histone-like proteins than with eukaryotic histone proteins. The phylogenetic tree indicated that the putative HU protein genes were acquired in Apicomplexa by means of a secondary endosymbiotic event from red algae and later they were transfered from the apicoplast organelle to the nuclear genome.
Abstract: The NS5B protein of classical swine fever virus (CSFV) is an important enzyme bearing a unique RNA-dependent RNA polymerase (RdRp) activity. The RdRp plays a crucial role in the viral replication cycle and in forming a replicase complex. However, the initiating synthesis mechanism of the CSFV RNA polymerase is unclearly described at present. Our aim is to reveal the RdRp-GTP docking sites and the effective modules of GTP initially bound to the polymerase in starting initiation…of replication according to a well predicted CSFV RdRp model. Based on some known crystal structures of RNA polymerase, computational methods were used to establish the model of a CSFV RdRp. An analogous mechanism of CSFV RNA polymerase in de novo initiation was subsequently represented through docking a GTP into the structure model. The unique GTP binding pocket of the polymerase was pointed out: five residues E227, S408, R427, K435, and R439 involved in steady hydrogen bonds and two residues C407 and L232 involved in hydrophobic contact with the GTP. From a genetic evolutionary point of view, three residues C407, S408 and R427 have been suggested to be of particular importance by analysis of residue conservation. It is suggested that these crucial residues should have very significant function in the de novo initiation of the rigorous CSFV polymerase model, which can lead us to design experiments for studying the mechanism of viral replication and develop valid anti-viral drugs.
Abstract: Understanding the mechanism of regulation of cancer genes and the constraints on their coding sequences is of fundamental importance in understanding the process of tumour development. Here we test the hypothesis that tumour suppressor genes and proto-oncogenes, due to their involvement in tumourigenesis, have distinct patterns of regulation and coding selective constraints compared to non-cancer genes. Indeed, we found significantly greater conservation in the promoter regions of proto-oncogenes, suggesting that…these genes are more tightly regulated, i.e. they are more likely to contain a higher density of cis-regulatory elements. Furthermore, proto-oncogenes appear to be preferentially targeted by microRNAs and have longer 3' UTRs. In addition, proto-oncogene evolution appears to be highly constrained, compared to tumour suppressor genes and non-cancer genes. A number of these trends are confirmed in breast and colon cancer gene sets recently identified by mutational screening.
Keywords: Oncogenes, tumour suppressor genes, gene regulation, sequence conservation, microRNAs, breast cancer, colon cancer
Abstract: Graphical methods are useful for visualizing signaling networks derived from the synthesis of large bodies of literature information or large-scale experimental measurements. Software tools to filter and organize these networks allow the exploration of their inherent biological and structural properties. We have developed NetAtlas, an open-source, Java-based Cytoscape plugin for examining signaling networks in the context of tissue gene expression patterns. The tissue gene expression data available through NetAtlas consists of 79…human tissues, 61 mouse tissues, and 44 combined tissues from 3 rat strains. Users may also import their own tissue gene expression data. The NetAtlas plugin allows the creation of tissue-defined signaling networks by identifying which components are expressed in particular tissues, which components show tissue-specific expression, and which components within the network are coordinately expressed across tissues. The NetAtlas plugin is available at http://sourceforge.net/projects/netatlas/.
Abstract: Microarray technology has become employed widely for biological researchers to identify genes associated with conditions such as diseases and drugs. To date, many methods have been developed to analyze data covering a large number of genes, but they focus only on statistical significance and cannot decipher the data with biological concepts. Gene Ontology (GO) is utilized to understand the data with biological interpretation; however, it is restricted to specific ontology such as biological process, molecular function,…and cellular component. Here, we attempted to apply MeSH (Medical Subject Headings) to interpret groups of genes from biological viewpoint. To assign MeSH terms to genes, in this study, contexts associated with genes are retrieved from full set of MEDLINE data using machine learning, and then extracted MeSH terms from retrieved articles. Utilizing the developed method, we implemented a software called BioCompass. It generates high-scoring lists and hierarchical lists for diseases MeSH terms associated with groups of genes to utilize MeSH and GO tree, and illustrated a wiring diagram by linking genes with extracted association from articles. Researchers can easily retrieve genes and keywords of interest, such as diseases and drugs, associated with groups of genes. Using retrieved MeSH terms and OMIM in conjunction with, we could obtain more disease information associated with target gene. BioCompass helps researchers to interpret groups of genes such as microarray data from a biological viewpoint.
Abstract: Microarray technology is a widely used approach to gene expression analysis. Many tools for microarray management and data analysis have been developed, and recently new methods have been proposed for deciphering biological pathways by integrating microarray data with other data sources. However, to improve microarray analysis and provide meaningful gene interaction networks, integrated software solutions are still needed. Therefore, we developed M@IA, an environment for DNA microarray data analysis allowing gene network…reconstruction. M@IA is a microarray integrated application which includes all of the steps of a microarray study, from MIAME-compliant raw data storage and processing gene expression analysis. Furthermore, M@IA allows automatic gene annotation based on ontology, metabolic/signalling pathways, protein interaction, miRNA and transcriptional factor associations, as well as integrative analysis of gene interaction networks. Statistical and graphical methods facilitate analysis, yielding new hypotheses on gene expression data. To illustrate our approach, we applied M@IA modules to microarray data taken from an experiment on liver tissue. We integrated differentially expressed genes with additional biological information, thus identifying new molecular interaction networks that are associated with fibrogenesis. M@IA is a new application for microarray management and data analysis, offering functional insights into microarray data by the combination of gene expression data and biological knowledge annotation based on interactive graphs. M@IA is an interactive multi-user interface based on a flexible modular architecture and it is freely available for academic users at http://maia.genouest.org.
Keywords: Microarray, database, systems biology, KEGG, gene ontology