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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: Biochemical studies to date have not been able to identify the linker histone H1 protein in the budding yeast Saccharomyces cerevisiae. Database homology searching against the complete yeast genome has identified a gene, HHO1, (or YPL127C, formerly LPI17) which encodes a protein that has two regions that show similarity to the pea histone H1 globular domain. To determine whether Hho1p can assume the shape of an H1 protein, homology model building experiments were performed using the…structure of chicken histone H5 globular domain as the basis for comparison. A statistically significant match between each of the two globular domains of Hho1p and the chicken histone H5 structure was obtained, and probability values indicate that there is a less than 1 in 100 chance that such a match would be the result of a random event. These findings support the proposal that Hho1p acts as an "H1 dimer" and could be responsible for the decreased linker DNA length observed between nucleosomal core particles.
Keywords: histone H1, HHO1, homology model building, protein structure prediction
Abstract: SEView is a Java applet that represents known or predicted elements of a protein or nucleotide sequence. It replaces or supplements the textual format of databases or program output with an interactive, graphical representation that is easily available through a WWW browser. Independence from the source data's format is achieved through a description language and ad hoc translators, which make the system versatile and flexible
Abstract: Tissue-specific gene expression is governed by enhancer and promoter sequences determining the specificity most probably by their internal organization of transcription factor binding sites. In case of muscle-specific gene expression excellent compilations of sequence regions responsible for the tissue-specificity are available. We took advantage of such a compilation in order to elucidate organizational features that are directly correlated with promoter specificity. We chose a systematic approach solely based on a sequence collection…known to consist of specific regulatory regions which can in principle be applied to every precompiled set of such sequences. We were able to show that these sequences contained a detectable subgroup (actin promoters) for which it was possible to construct a highly specific promoter model recognizing the majority of all known actin sequences. The model was robust with respect to different training sets, almost 100% specific and sensitive enough to be suitable for database searches. We believe this pilot study demonstrates the general applicability of our approach as well as the concept of modular promoter organization.
Abstract: Today different database systems for molecular structures (genes and proteins) and metabolic pathways are available. All these systems are characterized by the static data representation. For progress in biotechnology the dynamic representation of this data is important. The metabolism can be characterized as a complex biochemical network. Different models for the quantitative simulation of biochemical networks are discussed, but no useful formalization is available. This paper shows that the theory of Petrinets is…useful for the quantitative modeling of biochemical networks
Keywords: bioinformatics, biochemical networks, modeling and simulation, petrinets
Abstract: Functional annotation of proteins encoded in newly sequenced genomes can be expected to meet two conflicting objectives: (i) provide as much information as possible, and (ii) avoid erroneous functional assignments and over-predictions. The continuing exponential growth of the number of sequenced genomes makes the quality of sequence annotation a critical factor in the efforts to utilize this new information. When dubious functional assignments are used as a basis for subsequent predictions, they tend to proliferate,…leading to "database explosion". It is therefore important to identify the common factors that hamper functional annotation. As a first step towards that goal, we have compared the annotations of the Mycoplasma genitalium and Methanococcus jannaschii genomes produced in several independent studies. The most common causes of questionable predictions appear to be: i) non-critical use of annotations from existing database entries; ii) taking into account only the annotation of the best database hit; iii) insufficient masking of low complexity regions (e.g. non-globular domains) in protein sequences, resulting in spurious database hits obscuring relevant ones; iv) ignoring multi-domain organization of the query proteins and/or the database hits; v) non-critical functional inferences on the basis of the functions of neighboring genes in an operon; vi) non-orthologous gene displacement, i.e. involvement of structurally unrelated proteins in the same function. These observations suggest that case by case validation of functional annotation by expert biologists remains crucial for productive genome analysis.