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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: Men and women differ not only in their physical attributes and reproductive functions but also in many other characteristics, including the risks for some diseases as well as response to certain therapeutic treatments. Though genetically-identical for autosomal chromosomes, males and females could have gender-specific transcriptional or translational regulation, leading to differential mRNAs or protein products for some genes. To illustrate the gender-specific differences in mRNA-level expression, we compared gene expression patterns between…males and females using a whole-genome microarray dataset on the unrelated HapMap lymphoblastoid cell lines derived from individuals of European (58 individuals) and African (59 individuals) ancestry. We applied the Gene Set Enrichment Analysis to identify any overrepresented predefined gene sets in either men or women. Distinct patterns of upregulation and downregulation of certain chromosomal regions and other gene sets such as targets for certain microRNAs and transcription factors were identified in males or females, suggesting their potential roles in defining the gender-specific phenotypes. Gender-specific patterns of gene expression also appeared to be different between these two populations.
Abstract: Biological systems are extremely complex and often involve thousands of interacting components. Despite all efforts, many complex biological systems are still poorly understood. However, over the past few years high-throughput technologies have generated large amounts of biological data, now requiring advanced bioinformatic algorithms for interpretation into valuable biological information. Due to these high-throughput technologies, the study of biological systems has evolved from focusing on single components (e.g. genes) to encompassing large sets…of components (e.g. all genes in an entire genome), with the aim to elucidate their interdependences in various biological processes. In addition, there is also an increasing need for integrative analysis, where knowledge about the biological system is derived by data fusion, using heterogeneous data sets as input. We here review representative examples of bioinformatic methods for fusion-oriented interpretation of multiple heterogeneous biological data, and propose a classification into three categories of tasks that they address: data extraction, data integration and data fusion. The aim of this classification is to facilitate the exchange of methods between systems biology and other information fusion application areas.
Keywords: Information fusion, data fusion, data integration, systems biology
Abstract: We propose a new method to compare sequences of protein families by generating numerical characterizations through a 20D representation. Using a walk along the axes representing the amino acids we generate a vector for each sequence whose components can be used to derive distance matrices between sequences and whose magnitudes can be used to compare the similarities/dissimilarities between the different sequences. The distance matrices enable creation of phylogenetic trees without need for multiple…alignments or any other model dependencies. In this paper we test this technique with human globin gene sequences and then apply the method to a contemporary issue of evolutionary relationships of rat and human voltage-gated sodium channel α subunits and compare with published literature. The close match of the results demonstrates the reliability and ease of use of this method.
Abstract: Distant evolutionary relationships between proteins with low sequence similarity are difficult to recognise by computational methods. Consequently, many sequences obtained from large-scale sequencing projects cannot be assigned to any known proteins or families despite being evolutionarily related. To boost sensitivity, various sequence-based methods have been modified to make use of the better conserved secondary structure. Most of these methods are instance-based or generative. Here, we introduce a kernel-based remote homology detection method…that allows for a combination of sequence and secondary-structure similarity scores in a discriminative approach. We studied the ability of the method to predict superfamily membership as defined by the SCOP database. We show that a kernel method that combined sequence similarity scores with predicted secondary-structure similarity scores performed similar to a classifier that used scores calculated from sequences and true secondary structures, but performed better than a sequence-only based classifier and achieved a better mean than recently published results on the same data-set. It can be concluded that SVM classifiers trained to predict homology between distantly related proteins, become more accurate, if a joint sequence/secondary-structure similarity score approach is used.
Keywords: Remote homology detection, support vector machines, secondary structures
Abstract: Naturally occurring peptidases from organisms living under extreme conditions are adapted to function in environmental extremes, including temperature, salinity, pH, or pressure. These organisms represent unique sources for new bio-molecules that have both industrial and medicinal application. Adaptive strategies for functioning under extreme conditions are reflected at the enzyme sequence and structural level. Understanding the determinants responsible for unique functional features can be used to enhance the functional features of known proteins.…In the present study, the amino acid sequences of 81 peptidases of the thermolysin (M4) family were analyzed for possible determinants of psychrophilic and thermophilic features, by comparing with thermolysin from Bacillus thermoproteolyticus, the prototype enzyme of the family. The analysis indicated that M4 peptidases from cold-adapted species have fewer arginines and more lysines, and also fewer tyrosines and more phenylalanines than the prototype thermolysin. However, the opposite was true for M4 peptidases from thermophilic species. For sequences from thermophilic species the ratio of the seven amino acids I,V,Y,W,R,E,L were correlated to optimal growth temperature.
Abstract: Typical high-abundant proteins, including albumin, IgG, IgA and others, are the target of depletion methods usually applied to two-dimensional electrophoresis (2DE) of human biological fluids like serum and plasma. Detection of low-abundant proteins is of interest with regard to biomarkers for disease when being studied by 2DE or liquid chromatography-mass spectrometry (LC/MS). After depletion of very abundant proteins, serum samples consist of an enriched pool of low-abundant proteins that can be further studied without…significant interferences, thus allowing for a full identification of the low abundant proteins, whose spots become now more visible. We have employed wavelet-based techniques and their derived denoisers to explore 2DE from disease-control human samples. We have pursued the goal of mimicking in silico the spot detection performance experimentally obtained by depletion methods, thus hoping to read through the critical high-abundant protein regions. Our results suggest that an efficient and effective computational tool has been added to other ones performing 2DE image analysis, such as decomposition and segmentation, but with the advantage of being specifically targeted to the depletion task.
Abstract: The polyadenylation signal plays a key role in determining the site for addition of a polyadenylated tail to nascent mRNA and its mutation(s) are reported in many diseases. Thus, identifying poly(A) sites is important for understanding the regulation and stability of mRNA. In this study, Support Vector Machine (SVM) models have been developed for predicting poly(A) signals in a DNA sequence using 100 nucleotides, each upstream and downstream of this signal. Here, we introduced a novel…split nucleotide frequency technique, and the models thus developed achieved maximum Matthews correlation coefficients (MCC) of 0.58, 0.69, 0.70 and 0.69 using mononucleotide, dinucleotide, trinucleotide, and tetranucleotide frequencies, respectively. Finally, a hybrid model developed using a combination of dinucleotide, 2nd order dinucleotide and tetranucleotide frequencies, achieved a maximum MCC of 0.72. Moreover, for independent datasets this model achieved a precision ranging from 75.8–95.7% with a sensitivity of 57%, which is better than any other known methods.
Keywords: Polyadenylation signals, mRNA, Support Vector Machine (SVM), Matthews correlation coefficient (MCC), ROC plot, nucleotide frequency