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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: We report on a knowledge-based pathway-finding system that builds on the cell-signaling networks database, CSNDB, which we developed previously. This new system, PaF-CSNDB, uses a general inference engine to apply rules for finding and coupling pathways between or around specific biomolecules from the CSNDB database. We show how PaF-CSNDB finds relationships in a large but fragmented collection of cell-signaling knowledge by filtering out and composing together those sections of pathways specified from an extensive and…complex set of binary or pair-wise cell-signaling reactions. The system can be accessed over the World Wide Web (http://goc.nihs.go.jp/csndb.html).
Keywords: cellular signal transduction, knowledge-based expert system shell, pathway representation
Abstract: This paper presents theoretical and computational tools to understand how a small group of proteins, the death factors, are involved in widely different behavior of the cell. Experiments were done using a virtual laboratory that can simulate cellular response to different external stimuli. WARNING: It is not certain which of the theoretical protein clusters described here really occur in nature. In addition, the rules of cluster assembly are combinatorial, and thus an oversimplification to describe the…real situation.
Keywords: regulatory proteins, death factors, virtual experiments, signalization pathway
Abstract: CoPreTHi is a Java based web application, which combines the results of methods that predict the location of transmembane segments in protein sequences into a joint prediction histogram. Clearly, the joint prediction algorithm, produces superior quality results than individual prediction schemes. The program is available at http://o2.db.uoa.gr/CoPreTHi.
Abstract: The homeodomain is a common structural motif found in many transcription factors involved in cell fate determination during development. We have used threading analysis techniques to predict whether the atypical homeodomain of prospero (pros) family members could form the three-helical homeodomain structural motif, even though these proteins are not statistically similar to canonical homeodomains as assessed by BLAST searches. Amino acid sequences of these divergent homeodomain proteins were threaded through the X-ray…coordinates of the Drosophila engrailed homeodomain protein . The analysis confirms that the prospero class of homeodomain proteins is indeed capable of forming the homeodomain structure despite its low degree of sequence identity to the canonical homeodomain. Energy calculations indicate that the homeodomain structure is stabilized primarily by hydrophobic interactions between residues at the helical interfaces. Although the atypical prospero-type homeodomain shows very little sequence similarity when compared to other homeodomain proteins, the critical amino acids responsible for maintaining the three-dimensional structure are highly conserved. A number of other homeodomain proteins, such as PHO2p from Saccharomyces and Pax6 from human, were also included in the threading analysis and were shown to be able to form the engrailed structure, indicating that there are no rigid overall sequence requirements for the formation of the homeodomain structural motif. Based on the threading experiments and the subsequent structural alignment, a new amino acid signature that unambiguously identifies the prospero-type proteins was deduced.
Keywords: homeodomain, prospero, protein threading, structure prediction