Volume 7, Issue 1

Gene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.

Data management in the life sciences has evolved from simple storage of data to complex information systems providing additional functionalities like analysis and visualization capabilities, demanding the integration of statistical tools. In many cases the used statistical tools are hard-coded within the system. That leads to an expensive integration, substitution, or extension of tools because all changes have to be done in program code. Other systems are using generic solutions for tool integration but adapting them to another system is mostly rather extensive work. This paper shows a way to provide statistical functionality over a statistics web service, which can be easily integrated in any information system and set up using XML configuration files. The statistical functionality is extendable by simply adding the description of a new application to a configuration file. The service architecture as well as the data exchange process between client and service and the adding of analysis applications to the underlying service provider are described. Furthermore a practical example demonstrates the functionality of the service.

With the large variety of Proteomics workflows, as well as the large variety of instruments and data-analysis software available, researchers today face major challenges validating and comparing their Proteomics data. Here we present a new generation of the ProteinScape TM bioinformatics platform, now enabling researchers to manage Proteomics data from the generation and data warehousing to a central data repository with a strong focus on the improved accuracy, reproducibility and comparability demanded by many researchers in the field. It addresses scientists` current needs in proteomics identification, quantification and validation. But producing large protein lists is not the end point in Proteomics, where one ultimately aims to answer specific questions about the biological condition or disease model of the analyzed sample. In this context, a new tool has been developed at the Spanish Centro Nacional de Biotecnologia Proteomics Facility termed PIKE (Protein information and Knowledge Extractor) that allows researchers to control, filter and access specific information from genomics and proteomic databases, to understand the role and relationships of the proteins identified in the experiments. Additionally, an EU funded project, ProDac, has coordinated systematic data collection in public standards-compliant repositories like PRIDE. This will cover all aspects from generating MS data in the laboratory, assembling the whole annotation information and storing it together with identifications in a standardised format.

One of the major challenges in bioinfomatics is to integrate and manage data from different sources as well as experimental microarray data and present them in a user-friendly format. Therefore, we present CardioVINEdb, a data warehouse approach developed to interact with and explore life science data. The data warehouse architecture provides a platform independent web interface that can be used with any common web browser. A monitor component controls and updates the data from the different sources to guarantee up-todateness. In addition, the system provides a “static” and “dynamic” visualization component for interactive graphical exploration of the data.

Type III Polyketide synthases (PKS) are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence, being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machine (SVM). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.

MicroRNAs (miRNAs/miRs) are important cellular components that regulate gene expression at posttranscriptional level. Various upstream components regulate miR expression and any deregulation causes disease conditions. Therefore, understanding of miR regulatory network both at upstream and downstream level is crucial and a resource on this aspect will be helpful. Currently available miR databases are mostly related to downstream targets, sequences, or diseases. But as of now, no database is available that provides a complete picture of miR regulation in a specific condition. Our miR regulation web resource (miReg) is a manually curated one that represents validated upstream regulators (transcription factor, drug, physical, and chemical) along with downstream targets, associated biological process, experimental condition or disease state, up or down regulation of the miR in that condition, and corresponding PubMed references in a graphical and user friendly manner, browseable through 5 browsing options. We have presented exact facts that have been described in the corresponding literature in relation to a given miR, whether it’s a feed-back/feed-forward loop or inhibition/activation. Moreover we have given various links to integrate data and to get a complete picture on any miR listed. Current version (Version 1.0) of miReg contains 47 important human miRs with 295 relations using 190 absolute references. We have also provided an example on usefulness of miReg to establish signalling pathways involved in cardiomyopathy. We believe that miReg will be an essential miRNA knowledge base to research community, with its continuous upgrade and data enrichment. This HTML based miReg can be accessed from: www.iioab-mireg.webs.com or www.iioab.webs.com/mireg.htm.

Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.

Biological pathways are crucial to much of the scientific research today including the study of specific biological processes related with human diseases. PathJam is a new comprehensive and freely accessible web-server application integrating scattered human pathway annotation from several public sources. The tool has been designed for both (i) being intuitive for wet-lab users providing statistical enrichment analysis of pathway annotations and (ii) giving support to the development of new integrative pathway applications. PathJam’s unique features and advantages include interactive graphs linking pathways and genes of interest, downloadable results in fully compatible formats, GSEA compatible output files and a standardized RESTful API.

Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).

Reaction-diffusion systems are mathematical models that describe how the concentrations of substances distributed in space change under the influence of local chemical reactions, and diffusion which causes the substances to spread out in space. The classical representation of a reaction-diffusion system is given by semi-linear parabolic partial differential equations, whose solution predicts how diffusion causes the concentration field to change with time. This change is proportional to the diffusion coefficient. If the solute moves in a homogeneous system in thermal equilibrium, the diffusion coefficients are constants that do not depend on the local concentration of solvent and solute. However, in nonhomogeneous and structured media the assumption of constant intracellular diffusion coefficient is not necessarily valid, and, consequently, the diffusion coefficient is a function of the local concentration of solvent and solutes. In this paper we propose a stochastic model of reaction-diffusion systems, in which the diffusion coefficients are function of the local concentration, viscosity and frictional forces. We then describe the software tool Redi (REaction-DIffusion simulator) which we have developed in order to implement this model into a Gillespie-like stochastic simulation algorithm. Finally, we show the ability of our model implemented in the Redi tool to reproduce the observed gradient of the bicoid protein in the Drosophila Melanogaster embryo. With Redi, we were able to simulate with an accuracy of 1% the experimental spatio-temporal dynamics of the bicoid protein, as recorded in time-lapse experiments obtained by direct measurements of transgenic bicoidenhanced green fluorescent protein.

Using the lac operon as a paradigmatic example for a gene regulatory system in prokaryotes, we demonstrate how qualitative knowledge can be initially captured using simple discrete (Boolean) models and then stepwise refined to multivalued logical models and finally to continuous (ODE) models. At all stages, signal transduction and transcriptional regulation is integrated in the model description. We first show the potential benefit of a discrete binary approach and discuss then problems and limitations due to indeterminacy arising in cyclic networks. These limitations can be partially circumvented by using multilevel logic as generalization of the Boolean framework enabling one to formulate a more realistic model of the lac operon. Ultimately a dynamic description is needed to fully appreciate the potential dynamic behavior that can be induced by regulatory feedback loops. As a very promising method we show how the use of multivariate polynomial interpolation allows transformation of the logical network into a system of ordinary differential equations (ODEs), which then enables the analysis of key features of the dynamic behavior.