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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: Olfaction of insects is currently recognized as the major area of research for developing novel control strategies to prevent mosquito-borne infections. A 3-dimensional model (3D) was developed for the salivary gland odorant-binding protein-2 of the mosquito Culex quinquefasciatus, a major vector of human lymphatic filariasis. A homology modeling method was used for the prediction of the structure. For the modeling, two template proteins were obtained by mGenTHERADER, namely the high-resolution X-ray crystallography…structure of a pheromone-binding protein (ASP1) of Apis mellifera L., [1R5R:A] and the aristolochene synthase from Penicillium roqueforti [1DI1:B]. By comparing the template protein a rough model was constructed for the target protein using MODELLER, a program for comparative modelling. The structure of OBP of the mosquito Culex quinquefasciatus resembles the structure of pheromone-binding protein ASP1 of Apis mellifera L., [1R5R:A]. From Ramachandran plot analysis it was found that the portion of residues falling into the most favoured regions was 86.0%. The predicted 3-D model may be further used in characterizing the protein in wet laboratory.
Abstract: A T-DNA based promoter trapped mutant has led to the identification of a novel lateral organ junction specific promoter upstream of the pentatricopeptide repeat (PPR) protein coding gene LOJ in Arabidopsis thaliana by our laboratory. Various in silico based prediction tools are employed to characterize the upstream sequence of the LOJ gene. Out of numerous cis-elements detected in the LOJ promoter a few are considered important based on the expression pattern of the…LOJ gene. These elements would provide a basis for designing experiments for more accurate promoter function annotation. A comparative search for conserved elements in the 5'-upstream region of few genes involved in lateral organ development and meristem related expression reveals few common relevant regulatory motifs. The coding region of the LOJ gene is intron-less and contains 19 PPR units. Based on in silico analysis, LOJ protein is predicted to be hydrophobic in nature and targeted to mitochondria. A partial 3D model of the LOJ protein has been suggested using a homology-based modeling program.
Keywords: Arabidopsis thaliana, Lateral Organ Junction gene, LOJ, pentatricopeptide repeat protein, promoter, cis-acting elements, in silico analysis
Abstract: Transcriptional regulatory network (TRN) discovery using information from a single source does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. A methodology, TRND, that integrates a preliminary TRN, gene expression data and gene ontology is developed to discover TRNs. The method is applied to a comprehensive set of expression data on B cell and a preliminary TRN that included 1,335 genes, 443 transcription factors (TFs) and…4032 gene/TF interactions. Predictions were obtained for 443 TFs and 9,589 genes. 14,616 of 4,247,927 possible gene/TF interactions scored higher than the imposed threshold. Results for three TFs, E2F-4, p130 and c-Myc, were examined in more detail to assess the accuracy of the integrated methodology. Although the training sets for E2F-4 and p130 were rather limited, the activities of these two TFs were found to be highly correlated and a large set of coregulated genes is predicted. These predictions were confirmed with published experimental results not used in the training set. A similar test was run for the c-Myc TF using the comprehensive resource www.myccancergene.org. In addition, correlations between expression of genes that encode TFs and TF activities were calculated and showed that the assumption of TF activity correlates with encoding gene expression might be misleading. The constructed B cell TRN, and scores for individual methodologies and the integrated approach are available at systemsbiology.indiana.edu/trndresults.
Keywords: Transcriptional regulatory network, gene expression data analysis, B cell, transcription factors, gene ontology
Abstract: Among the various research areas that comprise bioinformatics, systems biology is gaining increasing attention. An important goal of systems biology is the unraveling of dynamic interactions between components of living cells (e.g., proteins, genes). These interactions exist among others on genomic, transcriptomic, proteomic and metabolomic levels. The levels themselves are heavily interconnected, resulting in complex networks of different interacting biological entities. Currently, various bioinformatics tools exist which are able to perform a…particular analysis on a particular type of network. Unfortunately, each tool has its own disadvantages hampering it to be used consistently for different types of networks or analytical methods. This paper describes the conceptual development of an open source extensible software framework that supports visualization and exploration of highly complex genomic networks, like metabolic or gene regulatory networks. The focus is on the conceptual foundations, starting from requirements, a description of the state of the art of network visualization systems, and an analysis of their shortcomings. We describe the implementation of some initial modules of the framework and apply them to a biological test case in bacterial regulation, which shows the relevance and feasibility of the proposed approach.
Keywords: Gene regulatory networks, prokaryotes, visualization framework, information exploration
Abstract: P53 is probably the most important tumor suppressor known. Over the years, information about this gene has increased dramatically. We have built a comprehensive knowledgebase of p53, which aims to facilitate wet-lab biologists to formulate their experiments and new-comers to learn whatever they need about the gene and bioinformaticians to make new discoveries through data analysis. Using the information curated, including mutation information, transcription factors, transcriptional targets, and single nucleotide polymorphisms, we have…performed extensive bioinformatics analysis, and made several new discoveries about P53. We have identified point missense mutations that are over-represented in cancers, but lack of functional studies. By assessing the capability of six p53 transcriptional targets' tag SNPs selected from HapMap to capture SNPs obtained from National Institute of Environmental Health Sciences (NIEHS) Environmental Genome project and vice versa, we conclude that NIEHS data is a better source for tagSNP selections of these genes in future association studies. Analysis of microRNA regulation in the transcriptional network of the p53 gene reveals potentially important regulatory relationships between oncogenic microRNAs and transcription factors of p53. By mapping transcription factors of p53 to pathways involved in cell cycle and apoptosis, we have identified distinctive transcriptional controls of p53 in these two physiological states.
Abstract: The identification and validation of protein allergens have become more important nowadays as more and more transgenic proteins are introduced into our food chains. Current allergen prediction algorithms focus on the identification of single motif or single allergen peptide for allergen detection. However, an analysis of the 575 allergen dataset shows that most allergens contain multiple motifs. Here, we present a novel algorithm that detects allergen by making use of combinations of motifs. Sensitivity of 0.772…and specificity of 0.904 were achieved by the proposed algorithm to predict allergen. The specificity of the proposed approach is found to be significantly higher than traditional single motif approaches. The high specificity of the proposed algorithm is useful in filtering out false positives, especially when laboratory resources are limited.
Keywords: Allergen, allergen prediction, combination of motifs
Abstract: We demonstrate the first application of cellular automata to the secondary structure predictions of proteins. Cellular automata use localized interactions to simulate global phenomena, which resembles the protein folding problem where individual residues interact locally to define the global protein conformation. The protein's amino acid sequence was input into the cellular automaton and rules for updating states were evolved using a genetic algorithm. An optimized accuracy (Q3) for the RS126 and CB513 dataset of 58.21% and…56.51%, respectively, could be obtained. Thus, the current work demonstrates the applicability of a rather simple algorithm on a problem as complex as protein secondary structure prediction.
Keywords: Protein secondary structure prediction, protein folding, evolved cellular automata, genetic algorithms, secondary structure, protein fold, protein structure
Abstract: Molecular dynamics simulations have gained importance due to their ability to provide valuable insights into understanding structure-function relationships of biological macromolecules. With increasing computational speeds there has been a substantial demand for optimization of simulation algorithms to obtain results even faster. With this on one hand, the need for ease of operation lies on the other. GUI front end programs are important appurtenances to ease the use of command line programs. Effective use of…command line based programs requires basic knowledge of the UNIX shell and at least one of the UNIX based text editors, making it difficult for pure biologists to use them efficiently. GROMACS, a widely used suite of molecular dynamics simulation and analysis programs, is no exception to this. As a matter of fact, the increasing dependency of experimental procedures on computational methods for accentuating certain key experimental findings increases the need for interactivity in use of command-line based packages. GUIMACS is a Java based user-interactive front-end interface for the Linux version of GROMACS (version 3.3). GUIMACS runs as a standalone application with Multiple Document Interface (MDI) enabling its users to run and/or analyze multiple molecular dynamics simulations simultaneously.
Abstract: We developed a Perl-based tool called LyM to determine the best factor for changes in the expression level for each transcript across two sets of expression libraries. LyM includes a Bayesian framework that analyzes the prior and posterior probability density function for each transcript considering the size of the libraries. To find out the best factor for change in each distribution, LyM was implemented with a binary search. In this work we aimed to validate…the performance of LyM tool using SAGE libraries from different human tissues. The results were compared with those generated by DGED (Digital Gene Expression Displayer), which worked as the gold standard, on the same data set, to assess accuracy. SAGE libraries were selected from CGAP for the following tissues (normal versus tumor): breast, colon, lung and stomach, consisting of eight SAGE libraries and 381,569 tags. DGED analyses were performed with five arbitrary factors for gene expression in two expression libraries: 2, 4, 8, 16 and 32. The results were confronted using the ratio between LyM and DGED factors and were quantitatively well-matched. LyM was capable of retrieving the best value of F, a factor that represents the fold difference in the expression of a specific gene between two expression libraries, represented by its SAGE tags. However, the optimal value of F is only shown in DGED output after multiple manual interactions. As a result, there was a significant economy of time with the LyM binary search algorithm. In some anecdotal cases we observed that the differential expression levels reached values above 100-fold for a fixed value of P=0.05, an information that initially remained hidden in DGED. Finally, LyM proved to be relatively fast, portable to the standard workstation present in the molecular biology laboratory, assisting accurate and convenient gene search in expression experiments with minimal user interactions.
Abstract: Ab initio coding potential prediction in a bacterial genome is an important step in determining an organism's transcriptional regulatory function. Extensive studies of genes structure have been carried out in a few species such as. Escherichia coli, fewer resources exist in newly sequenced genomes like Wolbachia. A model of gene prediction trained on one species may not reflect the properties of other, distantly related prokaryotic organisms. These issues were encountered in the course of…predicting genes in the genome of Wolbachia, very important gramnegative bacteria that form intracellular inherited infections in many invertebrates. We describe a coding potential predictor based on artificial neural networks and we compare its performance by using different architectures, learning algorithms and parameters. We rely on a dataset of positive samples constructed from coding sequences and on a negative dataset consisted of all the intergenic regions that were not located between the genes of an operon. Both datasets, positive and negative, were output as fasta formatted files and were used for neural network training. The fast, adaptive, batch learning algorithm Resilient propagation, exhibits the best overall performance on a 64input-10hidden-1output nodes multi-layer perceptron neural network.