Purchase individual online access for 1 year to this journal.
Price: EUR N/A
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: EXProt (database for EXPerimentally verified Protein functions) is a new non-redundant database containing protein sequences for which the function has been experimentally verified. It is a selection of 3976 entries from the Prokaryotes section of the EMBL Nucleotide Sequence Database, Release 66, and 375 entries from the Pseudomonas Community Annotation Project (PseudoCAP). The entries in EXProt all have a unique ID number and provide information about the organism, protein sequence, functional annotation, link to…entry in original database, and if known, gene name and link to references in PubMed/Medline. The EXProt web page (http://www.cmbi.nl/EXProt) provides further details of the database and a link to a BLAST search (blastp & blastx) of the database. The EXProt entries are indexed in SRS (http://www.cmbi.nl/srs/) and can be searched by means of keywords. Authors can be reached by email (firstname.lastname@example.org).
Abstract: We have developed an efficient sequence-analysis system and a database system for clones obtained from full-length enriched cDNA libraries made by using the oligo-capping method. We developed a semi-automatic analysis system for 5'- and 3'-end sequences. It pre-processes raw sequences (vector cut and accurate-sequence region extraction), clusters the sequences, searches for similarities through public databases, annotates completeness of clones and analyzes the ORFs in the sequences. Newly developed or improved programs are used in each…step. A new program, ESTiMateFull is used to evaluate and to predict the sequence-fullness based on comparisons with mRNA and EST sequences, respectively. The ATGpr program is used to predict sequence-fullness based on statistical information. The combination of full-length enriched cDNA clones and ATGpr fullness prediction resulted in 70% accuracy in the specificity and the sensitivity of the fullness predictions. For the ORFs predicted by the ATGpr, the signal peptides are predicted and a motif search is performed by our new system. We also developed a program that assembles our sequences with dbEST sequences and developed a system to retrieve clones by the characteristics of the ORFs. As keywords, combination of various results of the analyses can be used for retrieval. And various results such as ORF features and database search results can be shown on the same screen by multiple displays. Full-length clones having interesting functions can thus be retrieved efficiently by using this system.
Abstract: We selected 10 transmembrane (TM) prediction methods (KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0 and HMMTOP 2.0) and re-assessed its prediction performance using a reliable dataset with 122 entries of experimentally-characterized TM topologies. Then, we improved prediction performance by a consensus prediction method. Prediction performance during re-assessment and consensus prediction were based on four attributes: (i) the number of transmembrane segments (TMSs), (ii) the number of TMSs plus TMS-position,…(iii) N-tail location and (iv) TM topology. We noted that hidden Markov model-based methods dominate over other methods by individual prediction performance for all four attributes. In addition, all top-performing methods generally were model-based. Among prokaryotic sequences, HMMTOP 2.0 solely topped among other methods with prediction accuracies ranging from 64% to 86% across all attributes. However, among eukaryotic sequences, prediction performance for all the attributes was relatively poor compared with prokaryotic ones. On the other hand, our results showed that our proposed consensus prediction method significantly improved prediction performance by, at least, an additional nine percentage points particularly among prokaryotic sequences for the number of TMS (84%), number of TMS and position (80%), and TM topology attributes (74%). Although our consensus prediction method improved also the prediction performance among eukaryotic sequences, the obtained accuracies for all attributes were relatively lower than that obtained by prokaryotic counterparts particularly for TM topology.
Abstract: Currently, prediction of transcription factor binding sites is widely done using matrices collected from literature. This leads to several problems. We cannot actively control the conservation of the matrices, we cannot systematically use all binding sites available, we do not know which sites were used and which were discarded in matrix construction, we cannot compare and evaluate matrices easily, we cannot detect redundancy and we cannot control sensitivity and specificity. So we are lacking control…during the identification process. In this paper a method to overcome these problems is proposed. It is assumed that each binding site has an unknown context which determines its sequence. This leads to the idea of constructing specific matrices for each sequence we are analysing. To do so we have to regard identification of binding sites as a general process, starting at a dataset of known binding sites and ending with the identification of a potential new binding site. In this paper such a process is presented. Besides overcoming the mentioned problems, the implementation also reaches a significantly higher accuracy than current approaches. Evaluations are done analysing all binding sites of TRANSFAC 3.5 public. The resulting tool AliBaba2 is available at http://wwwiti.cs.uni-magdeburg.de/~grabe/alibaba2.
Abstract: Transcriptional regulation depends on the binding of transcription factors to their corresponding binding sites. The response to cellular signals is often mediated by the cooperative binding of transcription factors to well defined regulatory modules consisting of at least two transcription factor binding sites. Such regulatory modules can be responsible for the common regulation of genes within a gene class or confer a common function to promoters belonging to different gene classes. We developed in silico…models representing a common framework of potential regulatory sites specific for one promoter class (actins). We also generated models for two different functional promoter modules both of which confer responsiveness to tumor necrosis factor (TNF) and interferon (IFN) to a variety of promoters. All models exhibited high selectivity, e.g. the mammalian muscle actin promoter model produced no false negatives in a database search.
Abstract: Amino acid sequences of ferritin subunits from three orders of insects (Diptera: Drosophila and Aedes; Lepidoptera: Calpodes and Manduca; and Homoptera: Nilaparvata) were obtained from the public database, and analyzed using structural modeling algorithms. Pattern recognition analysis identifies cell attachment, glycosylation, myristoylation, microbody targeting, phosphorylation, cAMP/cGMP dependent, protein kinase C, casein kinase, and tyrosine kinase sites in these subunits. The modeling analyses suggest that the insect heavy-chain homologues are similar to their vertebrate…analogues and retain all active sites, including the ferroxidase center. On the contrary, the insect light-chain homologues are different from their vertebrate counterparts, and show none of these features.Five á-helices were located in the Dipteran and Lepidopteran, but not in Homopteran ferritin subunits.