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: Recent findings suggest the possibility that tumors originate from cancer cells with stem cell properties. The cancer stem cell (CSC) hypothesis provides an explanation for why existing cancer therapies often fail in eradicating highly malignant tumors and end with tumor recurrence. Although normal stem cells and CSCs both share the capacity for self-renewal and multi-lineage differentiation, suggesting that CSC may be derived from normal SCs, the cellular origin of transformation of CSCs is debatable. Research…suggests that the tightly controlled balance of self-renewal and differentiation that characterizes normal stem cell function is dis-regulated in cancer. Additionally, recent evidence has linked an embryonic stem cell (ESC)-like gene signature with poorly differentiated high-grade tumors, suggesting that regulatory pathways controlling pluripotency may in part contribute to the somatic CSC phenotype. Here, we introduce expression profile bioinformatic analyses of mouse breast cells with CSC properties, mouse embryonic stem (mES) and induced pluripotent stem (iPS) cells with an emphasis on how study of pluripotent stem cells may contribute to the identification of genes and pathways that facilitate events associated with oncogenesis. Global gene expression analysis from CSCs and induced pluripotent stem cell lines represent an ideal model to study cancer initiation and progression and provide insight into the origin cancer stem cells. Additionally, insight into the genetic and epigenomic mechanisms regulating the balance between self-renewal and differentiation of somatic stem cells and cancer may help to determine whether different strategies used to generate iPSCs are potentially safe for therapeutic use.
Abstract: DmpR (dimethylphenol regulatory protein) is a member of the NtrC family of transcriptional activators and controls the transcription of the dmp operons in response to aromatic effector compounds. Secondary structure and fold recognition prediction of N-terminal A domain of this protein (210 amino acid) was performed in Genesilico Metaserver and 3DJury. The consensus result from these servers suggested MJ_1460 as a template. Three dimensional structures were generated from the sequence structure alignments of the…template and target protein with MODELLER. The results suggested that the N-terminal A domain of DmpR belongs to Muramoyl pentapeptide carboxypeptidase domain family. The binding interaction sites of the known effectors were predicted using protein-ligand docking. The proposed active site of N-terminal A domain of DmpR comprises of key residues such as Phe93, Glu127, Phe132, Ser160, Phe163, Met164, Arg166 and Pro189. The findings provide some direction to the experimental studies that aim to broaden the range of phenolic derivative which can be sensed by N-DmpR in order to improve the biodegradation potential.
Keywords: DmpR, fold recognition, V4R domain, MJ_1460, protein structure prediction
Abstract: A major problem in designing vaccine for the dengue virus has been the high antigenic variability in the envelope protein of different virus strains. In this study, a computational approach was adopted to identify a multi-epitope vaccine candidate against dengue virus that may be suitable for large populations in the dengue-endemic regions. Different bioinformatics tools were exploited that helped the identification of a conserved immunological hot-spot in the dengue envelope protein. The tools also rendered the…prediction of immunogenicity and population coverage to the proposed 'in silico' vaccine candidate against dengue. A peptide region, spanning 19 amino acids, was identified in the envelope protein which found to be conserved in all four types of dengue viruses. Ten proteasomal cleavage sites were identified within the 19-mer conserved peptide sequence and a total of 8 overlapping putative cytotoxic T cell (CTL) epitopes were identified. The immunogenicity of these epitopes was evaluated in terms of their binding affinities to and dissociation half-time from respective human leukocyte antigen (HLA) molecules. The HLA allele frequencies were studied among populations in the dengue endemic regions and compared with respect to HLA restriction patterns of the overlapping epitopes. The cumulative population coverage for these epitopes as vaccine candidates was high ranging from approximately 80% to 92%. Structural analysis suggested that a 9-mer epitope fitted well into the peptide-binding groove of HLA-A*0201. In conclusion, the 19-mer epitope cluster was shown to have the potential for use as a vaccine candidate against dengue.
Keywords: Dengue, epitope vaccine, bioinformatics, HLA, population coverage
Abstract: Antagonism of cannabinoid receptor-1 has emerged as a most promising therapeutic target for the development of anti-obesity drugs. In the present study, an in silico approach using decision tree, random forest and moving average analysis has been applied to a data set comprising of 76 analogues of substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles for development of models for prediction of antagonistic activity of cannabinoid receptor-1. A total of 46 2D and 3D molecular descriptors of diverse nature were employed for…decision tree and random forest analysis. The values of majority of these descriptors for each analogue involved in the dataset were computed using E-Dragon software (version 1.0). Random forest correctly classified the analogues into active and inactive with an accuracy of 95%. A decision tree was also utilized for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 99% and correctly predicted the cross-validated (10 fold) data with an accuracy up to 90%. Finally, three molecular descriptors of diverse nature (including best descriptor identified by decision tree analysis) were subsequently used to build suitable models using moving average analysis. These models resulted in the prediction of cannabinoid receptor-1 antagonistic activity with an accuracy of 95–96%. High predictability of proposed models offer vast potential for providing lead structures for the development of potent cannabinoid receptor-1 antagonists for the treatment of obesity.
Keywords: Molecular descriptors, topological descriptors, topochemical descriptors, E-Dragon software, Information content indices, substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles, moving average analysis, decision tree, random forest, anti-obesity drugs, biarylpyrazolyl oxadiazoles, cannabinoid receptors
Abstract: Bacterial true lipases having thermo and alkaline stability are highly attractive for their industrial production of pharmaceuticals, agrochemicals, cosmetics, and flavour. Staphylococcus aureus lipase (SAL3) remains active at temperatures 40–60°C, with an optimum temperature of 55°C and an optimum pH of 9.5 stable over a range of 5–12. Detailed understanding of the structure and insight into the activity of such lipase would aid in engineering lipases that would function in the desired…extreme industrial environments. In the present study, we carried out in silico characterization and structural modeling of SAL3 which is thermoactive, alkaline and detergent-stable. Comparison of SAL3 with other staphylococcal lipases indicates that SAL3 is a true lipase having the catalytic triad (residues Ser119, Asp310 & His352) and the calcium binding site (residues Asp351, Asp354, Asp359, Asp362 and Gly286). Conservation in sequence implies that interfacial activation mechanism is possible in SAL3 with the lid formed by helix (residues 180–196) and loop (residues 197–206). Three dimensional (3D) structure model of SAL3 has been predicted for the first time and aims at understanding its function and biochemical characteristics of possessing relatively high thermal and pH stability.