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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: PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a Web site that has been designed and implemented to support research and analysis workflows ranging from metabolome to transcriptome analysis. The site provides access to a growing collection of standardized measurements of metabolites obtained by using NMR, GC-MS, LC-MS, and CE-MS, and metabolomics tools that support related analyses (SpinAssign for the identification of metabolites by means of NMR, KNApSAcK for searches within metabolite…databases). In addition, the transcriptomics tools provide Correlated Gene Search, and Cluster Cutting for the analysis of mRNA expression. Use of the tools and database can contribute to the analysis of biological events at the levels of metabolites and gene expression, and we describe one example of such an analysis for Arabidopsis thaliana using the batch-learning self-organizing map (BL-SOM), which is provided via the Web site.
Abstract: A large-scale analysis of human polyadenylation signals was carried out in silico. The most canonical AAUAAA hexamer and its 11 single-nucleotide variants that are most frequent in human genes were used to search for polyadenylation signals in the terminal sequences. Out of 18,277 poly(A) sites that were identified from 26,414 human genes, 82.5% of the sites were found to contain at least one of these 12 hexamers as a polyadenylation signal within 40 nucleotides upstream of…the poly(A) site. The rest (17.5%) did not contain any of these hexamers, which suggests the existence of yet unknown signals. A total of 20,347 terminal sequences in close proximity to 12 polyadenylation signals were collected using modified EST clustering technique to establish a large-scale database of polyadenylation signals. To characterize the 12 hexamers, the locations of polyadenylation signals that were identified as "authentic" and the uracil contents of the downstream region of the signal were examined. Based on this analysis, the 11 variants of the canonical AAUAAA were identified as possibly forming "functional" signals as AAUAAA. Moreover, the observed frequency of 41.9% for AAUAAA was significantly lower than those of other reports, suggesting that the non-canonical variants are more important in the polyadenylation process than frequently recognized. Since the poly(A) sites processed by those non-canonical variants have not been generally annotated in major gene databases, it is important to determine whether the variant hexamers could work as polyadenylation signals that may be responsible for generating heterogeneity of mRNAs by alternative polyadenylation.
Abstract: Microarray gene expression datasets are continually being placed in public repositories. As a result, one of the most important emerging challenges is that which enables researchers to take full advantage of such previously accumulated data to discover or validate common genes in similar biological systems. In light of this we have designed the MaXlab software to not only cross-compare available array data from different laboratories but also extract further knowledge from gene expression patterns embedded within…published data. More importantly MaXlab offers a flexible and automated solution applicable for microarray technologies including cDNA and Affymetrix gene chips generating expression profiles for common genes with biological significance. We have identified several sets of genes previously unknown to be commonly expressed across studies investigating related biological questions. Among them is the identification of 17 genes involved in the dysregulation of immune tolerance including the crucial transcription factor Egr2. In addition, we have identified 175 genes commonly expressed in basal and luminal breast tumours in response to the chemotherapeutic drug doxorubicin. The universal expression and characterisation of these encouraging genes identified through MaXlab suggests that they may play a common role in the mechanism of disease and hence act as an incentive for further investigation for identifying potential therapeutic targets. Overall, MaXlab is an attractive application for molecular biologists extracting the intersection between microarray datasets together with the gene expression profiles, from which biologists are able to infer further biological insights. The software together with file formats and additional material is freely available at http://www.immuno-software.org.