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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: Viruses are major factors of human infectious diseases. Understanding of the structure-function correlation in viruses is important for the identification of potential anti-viral inhibitors and vaccine targets. In virology research, virus-related databases and bioinformatic analysis tools are essential for discerning relationships within complex datasets about viruses and host-virus interactions. Bioinformatic analyses on viruses include the identification of open reading frames, gene prediction, homology searching, sequence alignment, and motif and epitope recognition. The…predictions of features such as transmembrane domains, glycosylation sites, and protein secondary and tertiary structure are important for analyzing the structure-function relationship of proteins encoded in viral genomes. Biochemical pathway analysis can help elucidate information at the biological systems level. Microarray analysis provides methods for high throughput screening and gene expression profiling. Virus-related bioinformatics databases include those concerned with viral sequences, taxonomy, homologous protein families, structures, or dedicated to specific viruses such as influenza and herpes simplex virus (HSV). This review provides a guide and overview of computational programs for these analyses as a resource for genomics and proteomics studies in virology research. These resources are useful for understanding viral diseases, as well as for the design and development of anti-viral agents.
Abstract: Microsatellites are abundant across prokaryotic and eukaryotic genomes. However, comparative analysis of microsatellites in the organellar genomes of plants and their utility in understanding phylogeny has not been reported. The purpose of this study was to understand the organization of microsatellites in the coding and non-coding regions of organellar genomes of major cereals viz., rice, wheat, maize and sorghum. About 5.8–14.3% of mitochondrial and 30.5–43.2% of chloroplast microsatellites were observed in the…coding regions. About 83.8–86.8% of known mitochondrial genes had at least one microsatellite while this value ranged from 78.6–82.9% among the chloroplast genomes. Dinucleotide repeats were the most abundant in the coding and non-coding regions of the mitochondrial genome while mononucleotides were predominant in chloroplast genomes. Maize harbored more repeats in the mitochondrial genome, which could be due to the larger size of genome. A phylogenetic analysis based on mitochondrial and chloroplast genomic microsatellites revealed that rice and sorghum were closer to each other, while wheat was the farthest and this corroborated with the earlier reported phylogenies based on nuclear genome co-linearity and chloroplast gene-based analysis.
Abstract: Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that…are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.
Keywords: Three-way junctions, folding prediction by energy minimization
Abstract: In the past, a large number of methods have been developed for predicting various characteristics of a protein from its composition. In order to exploit the full potential of protein composition, we developed the web-server COPid to assist the researchers in annotating the function of a protein from its composition using whole or part of the protein. COPid has three modules called search, composition and analysis. The search module allows searching of protein sequences in six…different databases. Search results list database proteins in ascending order of Euclidian distance or descending order of compositional similarity with the query sequence. The composition module allows calculation of the composition of a sequence and average composition of a group of sequences. The composition module also allows computing composition of various types of amino acids (e.g. charge, polar, hydrophobic residues). The analysis module provides the following options; i) comparing composition of two classes of proteins, ii) creating a phylogenetic tree based on the composition and iii) generating input patterns for machine learning techniques. We have evaluated the performance of composition-based (or alignment-free) similarity search in the subcellular localization of proteins. It was found that the alignment free method performs reasonably well in predicting certain classes of proteins. The COPid web-server is available at http://www.imtech.res.in/raghava/copid/.
Abstract: Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the pre-protein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical…secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.
Abstract: Heterotrimeric G proteins interact with G protein-coupled receptors in response to stimulation by hormones, neurotransmitters, chemokines, and sensory signals to intracellular signaling cascades. Recently reported studies indicate that G protein subunits play a significant role in different eukaryotic diseases including inflammation, neurological diseases, cardiovascular diseases, endocrine disorders as well as plant pathogen response, infectious hyphae growth, differentiation and virulence of pathogenic fungi. Thus a study of their functions, signaling pathways, and protein…interactions may lead to the development of various preventive approaches. The diversity of α, β and γ subunits of G proteins necessitates a prediction algorithm that helps in the identification of new proteins such as Gβ where WD-40 repeats are not well characterized. The currently available techniques for finding G proteins are homology based search analyses and wet lab experiments, which are not very effective in finding new classes of proteins. We present here a robust computational method for finding new G proteins and their homologs using a SVM based pattern recognition algorithm. Several physicochemical and compositional properties including dipeptide, tripeptide and hydrophobicity composition are used for generating the SVM classifiers. This method has 96.17%, 95.38%, 97.6% sensitivity and 99.45%, 100%, 100% specificity on test sets for G protein α, β, and γ subunits, respectively. This algorithm correctly predicts the known α, β and γ subunits reported in literature. One important contribution of this algorithm is that it helps in improving genome annotation of several proteins as G proteins and serves as a useful tool for comparative genomic analysis of G proteins. Using this method, novel G protein subunits are predicted in 31 genomes covering plant, fungi and animal kingdom. The software is available at the website http://biomine.cs.uah.edu/bioinformatics/svm_prog/scripts/GProteins/vectorg.html. Supplementary files: The supplementary files are available on http://www.bioinfo.de/isb/2008/08/0013/supplementary_material/.
Keywords: Heterotrimeric G proteins, SVM, compositional properties, signal transduction
Abstract: Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns – sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold – computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature…databases, biological ontologies and other sources can help to focus on the most plausible patterns only. The paper proposes, implements and tests a flexible constraint-based framework that enables the effective mining and representation of meaningful over-expression patterns representing intrinsic associations among genes and biological situations. The framework can be simultaneously applied to a wide spectrum of genomic data and we demonstrate that it allows to generate new biological hypotheses with clinical implications.
Abstract: Systems biology approaches to bacteria require an integrated database and a bioinformatics tool platform to enable automated and manual annotation, regulatory and metabolic network deduction, and the storage of related experimental as well as predicted data. In this context ROSY – the Roseobacter SYstems biology database – was developed for completed and draft genomes of representatives of the marine Roseobacter clade, which constitutes one of the most abundant bacterial clades in the ocean. ROSY…provides an integrative view on comprehensive data collections such as KEGG, GenBank, RoseoBase, BRENDA, and PRODORIC as well as mediates the use of connected tools for promoter analysis (Virtual Footprint), genome and pathway visualization (CGView, PathCompare), and prediction of signal peptides (PrediSi). Moreover, metabolome, transcriptome, and proteome data can be stored in ROSY, supplying an integrated platform for comparative genomics and systems biology. This entire database system along with the data retrieval, comparative analysis, and website presentation tools (http://rosy.tu-bs.de) can be easily adopted for the systems biological analysis of other bacterial groups.
Abstract: Codon optimization is a generic technique to achieve optimum expression of a foreign gene in the host's cell system. Selection of optimum codons depends on codon usage of the host genome and the presence of several desirable and undesirable sequence motifs. Searching these motifs in all possible combinations of the codons increases the search space exponentially with respect to sequence length. GASCO is an algorithm developed for the optimum codon selection using genetic algorithms. The algorithm…reduces the search space and provides an approximate solution to the problem. The algorithm has applications in DNA vaccine design for successfully eliciting potent immune responses and synthetic gene design for metabolic pathway engineering. The software for the proposed algorithm is available on http://miracle.igib.res.in/gasco/.
Keywords: Codon optimization, genetic algorithm, DNA vaccine, CpG motif