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: About five years ago, ontology was almost unknown in bioinformatics, even more so in molecular biology. Nowadays, many bioinformatics articles mention it in connection with text mining, data integration or as a metaphysical cure for problems in standardisation of nomenclature and other applications. This article attempts to give an account of what concept ontologies in the domain of biology and bioinformatics are; what they are not; how they can be constructed; how they can be…used; and some fallacies and pitfalls creators and users should be aware of.
Abstract: Comparative sequence analysis is a powerful approach to identify functional elements in genomic sequences. Herein, we describe AGenDA (Alignment-based GENe Detection Algorithm), a novel method for gene prediction that is based on long-range alignment of syntenic regions in eukaryotic genome sequences. Local sequence homologies identified by the DIALIGN program are searched for conserved splice signals to define potential protein-coding exons; these candidate exons are then used to assemble complete gene structures. The…performance of our method was tested on a set of 105 human-mouse sequence pairs. These test runs showed that sensitivity and specificity of AGenDA are comparable with the best gene- prediction program that is currently available. However, since our method is based on a completely different type of input information, it can detect genes that are not detectable by standard methods and vice versa. Thus, our approach seems to be a useful addition to existing gene-prediction programs. Availability: DIALIGN is available through the Bielefeld Bioinformatics Server (BiBiServ) at  The gene-prediction program AGenDA described in this paper will be available through the BiBiServ or MIPS web server at http://mips.gsf.de.
Abstract: GOBASE is a relational database that integrates data associated with mitochondria and chloro-plasts. The most important data in GOBASE, i. e., molecular sequences and taxonomic information, are obtained from the public sequence data repository at the National Center for Biotechnology Information (NCBI), and are validated by our experts. Maintaining a curated genomic database comes with a towering labor cost, due to the shear volume of available genomic sequences and the plethora of annotation errors and omissions…in records re-trieved from public repositories. Here we describe our approach to increase automation of the database population process, thereby reducing manual intervention. As a first step, we used Unified Modeling Language (UML) to construct a list of potential errors. Each case was evaluated independently, and an expert solution was devised, and represented as a diagram. Subsequently, the UML diagrams were used as templates for writing object-oriented automation programs in the Java programming language.
Abstract: A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter…all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" . SEMEDA can be found at . We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.
Abstract: A method has been developed for constructing a tree source model for genetic text generation. Model visualisation in the form of suffix (context) trees provides a new way of context analysis of symbol sequences. Estimation of the stochastic complexity of the data in the frame of the model serves as a criterion for the model's ascertainment. The model and complexity values are used for analysis of genetic texts. The software realisation of this algorithm enables to…reveal statistical properties of genetic sequences based on an information measure. The program developed is available via Internet at http://wwwmgs.bionet.nsc.ru/mgs/programs/complexity/.
Keywords: complexity, information measure, suffix tree visualisation, variable memory Markov model, genetic texts, statistical modelling
Abstract: The combination of full-scale genomic sequencing with high throughput expression analysis provides a new and largely unexploited basis for in silico functional genomics. Recent break through developments in locat-ing and analyzing promoters now allow extending functional genomics in silico far beyond identification of protein sequences into the complex regulatory structures and mechanisms of the genome. However, only first examples of this new type of approach are emerging at present and intensive further developments…of bioinformatics tools will be required before such analysis can become large-scale routine in genomic sequence analysis. Nevertheless, the door to a new dimension of functional analysis of the genomic sequence is open. Finally, only the tight integration of the enormous amount of knowledge gained from proteins sequence analysis with the complementary information about gene regulation will afford us with a more complete picture of the networks than constitute life.