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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: Distinguishing between acute and chronic HCV infections is clinically important given that early treatment of infected patients leads to high rates of sustained virological response. Analysis of 2179 clonal sequences derived from hypervariable region 1 (HVR1) of the HCV genome in samples obtained from patients with acute (n = 49) and chronic (n = 102) HCV infection showed that intra-host HVR1 diversity was 1.8 times higher in patients with chronic than acute infection. Significant differences in frequencies of 5 amino acids (positions 5, 7, 12, 16 and 18) and the average genetic distances among intra-host HVR1 variants were found using…analysis of molecular variance. Differences were also observed in the polarity, volume and hydrophobicity of 10 amino acids (at positions 1, 4, 5, 12, 14, 15, 16, 21, 22 and 29). Based on these properties, a classification model could be constructed, which permitted HVR1 variants from acute and chronic cases to be discriminated with an accuracy of 88%. Progression from acute to chronic stage of HCV infection is accompanied by characteristic changes in amino acid composition of HVR1. Identifying these changes may permit diagnosis of recent HCV infection.
Keywords: HCV infection, acute, chronic, quasispecies, hypervariable region 1
Abstract: The detection of compensatory mutations that abrogate negative fitness effects of drug-resistance and vaccine-escape mutations indicates the important role of epistatic connectivity in evolution of viruses, especially under the strong selection pressures. Mapping of epistatic connectivity in the form of coordinated substitutions should help to characterize molecular mechanisms shaping viral evolution and provides a tool for the development of novel anti-viral drugs and vaccines. We analyzed coordinated variation among amino acid sites in 370 the hepatitis B virus (HBV) polymerase sequences using Bayesian networks. Among the HBV polymerase domains the spacer domain separating terminal protein from the reverse-transcriptase domain, showed…the highest network centrality. Coordinated substitutions preserve the hydrophobicity and charge of Spacer. Maximum likelihood estimates of codon selection showed that Spacer contains the highest number of positively selected sites. Identification of 67% of the domain lacking an ordered structure suggests that Spacer belongs to the class of intrinsically disordered domains and proteins whose crucial functional role in the regulation of transcription, translation and cellular signal transduction has only recently been recognized. Spacer plays a central role in the epistatic network associating substitutions across the HBV genome, including those conferring viral virulence, drug resistance and vaccine escape. The data suggest that Spacer is extensively involved in coordination of HBV evolution.
Abstract: Hepatitis C Virus sequence studies mainly focus on the viral amplicon containing the Hypervariable region 1 (HVR1) to obtain a sample of sequences from which several population genetics parameters can be calculated. Recent advances in sequencing methods allow for analyzing an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three recent technologies for amplicon analysis: (i) Next-Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry…of base-specific cleavage reactions of a target sequence. These three technologies were used to assess intra-host diversity and inter-host genetic relatedness in HVR1 amplicons obtained from 38 patients (subgenotypes 1a and 1b). Assessments of intra-host diversity varied greatly between sequence-based and mass-spectrometry-based data. However, assessments of inter-host variability by all three technologies were equally accurate in identification of genetic relatedness among viral strains. These results support the application of all three technologies for molecular epidemiology and population genetics studies. Mass spectrometry is especially promising given its high throughput, low cost and comparable results with sequence-based methods.
Abstract: Next generation sequencing technologies have recently been applied to characterize mutational spectra of the heterogeneous population of viral genotypes (known as a quasispecies) within HIV-infected patients. Such information is clinically relevant because minority genetic subpopulations of HIV within patients enable viral escape from selection pressures such as the immune response and antiretroviral therapy. However, methods for quasispecies sequence reconstruction from next generation sequencing reads are not yet widely used and remains an emerging area of research. Furthermore, the majority of research methodology in HIV has focused on 454 sequencing, while many next-generation sequencing platforms used in practice are limited to…shorter read lengths relative to 454 sequencing. Little work has been done in determining how best to address the read length limitations of other platforms. The approach described here incorporates graph representations of both read differences and read overlap to conservatively determine the regions of the sequence with sufficient variability to separate quasispecies sequences. Within these tractable regions of quasispecies inference, we use constraint programming to solve for an optimal quasispecies subsequence determination via vertex coloring of the conflict graph, a representation which also lends itself to data with non-contiguous reads such as paired-end sequencing. We demonstrate the utility of the method by applying it to simulations based on actual intra-patient clonal HIV-1 sequencing data.
Abstract: Sequence heterogeneity substantially affects antigenic properties of the major epitope in the hepatitis C virus (HCV) NS3 protein. To facilitate protein engineering of NS3 antigens immunologically reactive with antibody against the broad diversity of HCV variants we constructed a set of Bayesian Networks (BN) for predicting antigenicity based on structural parameters. Using homology modeling, tertiary (3D) structures of NS3 variants with known antigenic properties were predicted. Energy force field estimated using the 3D-models was found to be most strongly associated with the antigenic properties. The best BN-models showed 100% accuracy of prediction of immunological reactivity with tested serum specimens in…10-fold cross validation. Bootstrap analyses of BN’s constructed using selected features showed that secondary structure and electrostatic potential assessed from 3D-models are the most robust attributes associated with immunological reactivity of NS3 antigens. The data suggest that the BN models may guide the development of NS3 antigens with improved diagnostically relevant properties.
Abstract: Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity…of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.
Abstract: Influenza A viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. In April 2009, a novel swine-origin H1N1 virus emerged in North America and caused the first pandemic of the 21st century. Toward the end of 2009, two waves of outbreaks occurred, and then the disease moderated. It will be critical to understand how this novel pandemic virus invaded and adapted to a human population. To understand the molecular dynamics and evolution in this pandemic H1N1 virus, we applied an Expectation-Maximization algorithm to estimate the Gaussian mixture…in the genetic population of the hemagglutinin (HA) gene of these H1N1 viruses from April of 2009 to January of 2010 and compared them with the viruses that cause seasonal H1N1 influenza. Our results show that, after it was introduced to human population, the 2009 H1N1 viral HA gene changed its population structure from a single Gaussian distribution to two major Gaussian distributions. The breadths of HA genetic diversity of 2009 H1N1 virus also increased from the first wave to the second wave of this pandemic. Phylogenetic analyses demonstrated that only certain HA sublineages of 2009 H1N1 viruses were able to circulate throughout the pandemic period. In contrast, the influenza HA population structure of seasonal H1N1 virus was relatively stable, and the breadth of HA genetic diversity within a single season population remained similar. This study revealed an evolutionary mechanism for a novel pandemic virus. After the virus is introduced to human population, the influenza virus would expand their molecular diversity through both random mutations (genetic drift) and selections. Eventually, multiple levels of hierarchical Gaussian distributions will replace the earlier single distribution. An evolutionary model for pandemic H1N1 influenza A virus was proposed and demonstrated with a simulation.
Keywords: Mixture model analysis, influenza A virus, influenza pandemic, Expectation-Maximization, population structure, hemagglutinin
Abstract: This paper addresses the problem of reconstructing viral quasispecies from next-generation sequencing reads obtained from amplicons (i.e., reads generated from predefined amplified overlapping regions). We compare the parsimonious and likelihood models for this problem and propose several novel assembling algorithms. The proposed methods have been validated on simulated error-free HCV and real HBV amplicon reads. The new algorithms have been shown to outperform the method of Prosperi et. al . Our experiments also show that viral quasispecies can be reconstructed in most cases more accurately from amplicon reads rather than shotgun reads. All algorithms have been implemented and made available…at https://bitbucket.org/nmancuso/bioa/.
Abstract: The paper addresses the problem of how to use RNA-Seq data for transcriptome reconstruction and quantification, as well as novel transcript discovery in partially annotated genomes. We present a novel annotation-guided general framework for transcriptome discovery, reconstruction and quantification in partially annotated genomes and compare it with existing annotation-guided and genome-guided transcriptome assembly methods. Our method, referred as D iscovery and R econstruction of U nannotated T ranscripts (DRUT), can be used to enhance existing transcriptome assemblers, such as Cufflinks , as well as to accurately estimate the transcript frequencies. Empirical analysis on synthetic datasets confirms that Cufflinks enhanced by…DRUT has superior quality of reconstruction and frequency estimation of transcripts.
Keywords: Transcriptome reconstruction and quantification, RNA-Seq, next generation sequencing, expectation maximization
Abstract: Hepatitis C virus (HCV) is a major cause of liver disease world-wide. Current interferon and ribavirin (IFN/RBV) therapy is effective in 50%–60% of patients. HCV exists in infected patients as a large viral population of intra-host variants (quasispecies), which may be differentially resistant to interferon treatment. We present a method for measuring differential interferon resistance of HCV quasispecies based on mathematical modeling and analysis of HCV population dynamics during the first hours of interferon therapy. The mathematical models showed that individual intra-host HCV variants have a wide range of resistance to IFN treatment in each patient. Analysis of differential IFN…resistance among intra-host HCV variants allows for accurate prediction of response to IFN therapy. The models strongly suggest that resistance to interferon may vary broadly among closely related variants in infected hosts and therapy outcome may be defined by a single or a few variants irrespective of their frequency in the intra-host HCV population before treatment.
Abstract: Beta-lactamases are bacterial enzymes which impart resistance against β-lactam-antibiotics. CTX-Ms are the β-lactamases that target cephalosporin antibiotics (e.g. cefotaxime and ceftazidime) while SME-1, KPC-2, IMI-1 and SFC-1 target carbapenems. Clavulanic acid, sulbactam and tazobactam are traditional β-lactamase inhibitors while LN1-255 and NXL-104 whereas novel inhibitors, inhibiting the activity of these enzymes. Studying the binding pattern of these drugs is helpful in predicting the versatile inhibitors for betalactamases. The aims of the study were: describing the mode of interaction of CTX-M (modeled from the bla CTX-M gene of this study) and the said carbapenemases with their respective target drugs and…inhibitors and to perform an in silico comparison of the efficacies of traditional and novel β-lactamase-inhibitors based on fitness score. The bla CTX-M marker was PCR-amplified from plasmid DNA of E. coli strain isolated from community-acquired urinary tract infection. E. coli C600 cells (harboring cloned bla CTX-M ) were found positive for extended-spectrum-β-lactamase (ESBL) production by the double-disk-synergy test. The three dimensional structures of CTX-M-15, SME-1 and IMI-1 were predicted by Swiss Model Server. The interaction between selected structures and inhibitors was performed by GOLD 5.0. On the basis of the docking score and binding pattern, we conclude that compound LN1-255 followed by tazobactam is best inhibitor against all the selected target enzymes as compared to clavulanate, sulbactam and NXL-104. Five conserved amino acids, Ser70, Ser130, Lys235, Thr236 and Gly237 were found crucial in stabilizing the complexes through hydrogen bonding and hydrophobic interactions.
Abstract: MicroRNA expression profiles can improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. In this paper, we undertook to develop a miRNA-mRNA network and uncover unique growth suppressive miRNAs in lung cancer using microarray data. The miRNA-mRNA network was developed based on a bipartite graph theory approach, and a number of miRNA-mRNA modules have been identified to mine associations between miRNAs and mRNAs. From the network, we identified totally 29 protective miRNA-mRNA regulatory modules, since we restricted our search to protective miRNAs. Subsequently we analyzed the pathways for the target genes in the protective miRNA-mRNA modules using Pathway-Express.…The miRNA-mRNA network efficiently detects hub mRNAs deregulated by the protective miRNAs and identifies cancer specific miRNAs in lung cancer. From the pathway analysis results, the ECM receptor pathway, Focal adhesion pathway and cell adhesion molecules pathway seem to be more interesting to investigate, since these pathways were related to all the ten protective miRNAs. Furthermore, protective miRNA target analysis revealed that genes VCAN, SIL, CD44 and MMP14 were found to have an important role in these pathways. Hence, it was inferred that these genes can be important putative targets for those protective miRNAs. A greater understanding of the mechanisms regulating VCAN, SIL, CD44 and MMP14 expression and activity will assist in the development of specific inhibitors of cancer cell metastasis. Thus these observations are expected to have an intense implication in cancer and may be useful for further research.