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At least 451 records · Page 25Linked to original sources
PubMed2006

Topological properties of protein-protein and metabolic interaction networks of Drosophila melanogaster.

The underlying principle governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. A key feature of the scale-free architecture is the vitality of the most connected nodes (hubs). The major objective of this article was to analyze the protein-protein and metabolic interaction networks of Drosophila melanogaster by considering the architectural patterns and the consequence of removal of hubs on the topological parameter of the two interaction systems. Analysis showed that both interaction networks follow a scale-free model, establishing the fact that most real world networks, from varied situations, conform to the small world pattern. The average path length showed a two-fold and a three-fold increase (changing from 9.42 to 20.93 and from 5.29 to 17.75, respectively) for the protein-protein and metabolic interaction networks, respectively, due to the deletion of hubs. On the contrary, the arbitrary elimination of nodes did not show any remarkable disparity in the topological parameter of the protein-protein and metabolic interaction networks (average path length: 9.42+/-0.02 and 5.27+/-0.01, respectively). This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

Algorithms↗
PubMed1994

Biological evaluation of d2, an algorithm for high-performance sequence comparison.

A number of algorithms exist for searching sequence databases for biologically significant similarities based on the primary sequence similarity of aligned sequences. We have determined the biological sensitivity and selectivity of d2, a high-performance comparison algorithm that rapidly determines the relative dissimilarity of large datasets of genetic sequences. d2 uses sequence-word multiplicity as a simple measure of dissimilarity. It is not constrained by the comparison of direct sequence alignments and so can use word contexts to yield new information on relationships. It is extremely efficient, comparing a query of length 884 bases (INS1ECLAC) with 19,540,603 bases of the bacterial division of GenBank (release 76.0) in 51.77 CPU seconds on a Cray Y/MP-48 supercomputer. It is unique in that subsequences (words) of biological interest can be weighted to improve the sensitivity and selectivity of a search over existing methods. We have determined the ability of d2 to detect biologically significant matches between a query and large datasets of DNA sequences while varying parameters such as word-length and window size. We have also determined the distribution of dissimilarity scores within eukaryotic and prokaryotic divisions of GenBank. We have optimized parameters of the d2 program using Cray hardware and present an analysis of the sensitivity and selectivity of the algorithm. A theoretical analysis of the expectation for scores is presented. This work demonstrates that d2 is a unique, sensitive, and selective method of rapid sequence comparison that can detect novel sequence relationships which remain undetected by alternate methodologies.

Algorithms↗
PubMed2004

BioLingua: a programmable knowledge environment for biologists.

UNLABELLED: BioLingua is an interactive, web-based programming environment that enables biologists to analyze biological systems by combining knowledge and data through direct end-user programming. BioLingua embeds a mature symbolic programming language in a frame-based knowledge environment, integrating genomic and pathway knowledge about a class of similar organisms. The BioLingua language provides interfaces to numerous state-of-the-art bioinformatic tools, making these available as an integrated package through the novel use of web-based programmability and an integrated Wiki-based community code and data store. The pilot instantiation of BioLingua, which has been developed in collaboration with several cyanobacteriologists, integrates knowledge about a subset of cyanobacteria with the Gene Ontology, KEGG and BioCyc knowledge bases. We introduce the BioLingua concept, architecture and language, and give several examples of its use in complex analyses. AVAILABILITY: Extensive documentation is available online at http://nostoc.stanford.edu/Docs/index.html CONTACT: JShrager@Stanford.edu

Artificial Intelligence↗
PubMed2005

Design of a description language for generating wrapper to collect biological data.

The biological data are scattered in various areas with various formats and they are changing continuously. Therefore, data integration becomes an important issue to provide researcher a dynamic access of data. In the data integration process, the method of extracting heterogeneous data dynamically from the data source is an essential part. Data extraction method using wrapper can provide flexibility and extensibility to an integration system.

Computational Biology↗
PubMed2006

A top-level ontology of functions and its application in the Open Biomedical Ontologies.

MOTIVATION: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. RESULTS: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

Biomedical Engineering↗
PubMed2005

Interactome: gateway into systems biology.

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

Animals↗
PubMed2006

Integrating biological data through the genome.

Owing to the ongoing success of the genome sequencing and structural genomics projects, the increase in both sequence and structural data is rapid. The development of tools for the annotation of sequence and structural data has become more important in the hope of keeping up with this data explosion. Scientists in this field have addressed these issues over the last 10 years and there now exists a wealth of methods and approaches to help interpret these data. However, there is no current way in which these methods can be incorporated easily so that the resulting annotations can be viewed together. This review discusses the development of these annotation methods and introduces the BioSapiens Network of Excellence, which has been formed in order to integrate the methods which have been developed in Europe.

Animals↗
PubMed2002

Geometric and statistical properties of the mean-field hydrophobic-polar model, the large-small model, and real protein sequences.

Lattice models, for their coarse-grained nature, are best suited for the study of the "designability problem," the phenomenon in which most of the about 16 000 proteins of known structure have their native conformations concentrated in a relatively small number of about 500 topological classes of conformations. Here it is shown that on a lattice the most highly designable simulated protein structures are those that have the largest number of surface-core switchbacks. A combination of physical, mathematical, and biological reasons that causes the phenomenon is given. By comparing the most foldable model peptides with protein sequences in the Protein Data Bank, it is shown that whereas different models may yield similar designabilities, predicted foldable peptides will simulate natural proteins only when the model incorporates the correct physics and biology, in this case if the main folding force arises from the differing hydrophobicity of the residues, but does not originate, say, from the steric hindrance effect caused by the differing sizes of the residues.

Amino Acid Sequence↗
PubMed2004

Towards cooperative frameworks for modeling and integrating biological processes knowledge.

Data organization has become a strategic target for biologists due to the increasing volume of genomic data available for them. For this purpose, we need a complete knowledge model for representing biological system. In this paper, we deal with both processes for the creation and integration of shareable, reusable domain models within biology, which is a critical issue. In particular, this work introduces a new cooperative development approach for biology ontologies. This approach is based on the integration of the ontologies supplied by different human experts. Two experiments in biological domains are presented and their results discussed.

Artificial Intelligence↗
PubMed2005

Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function.

An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein-protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein-protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein-protein interactions.

Animals↗
PubMed2002

Computational function assignment for potential drug targets: from single genes to cellular systems.

Biomedical science is currently undergoing an epoch-marking transition from its classical phase to the post-genome era. The outstanding success of world-wide genome sequencing efforts, evidenced by the recent publication of the draft of the human genome, together with the completion of several genomes of eukaryotic model organisms and the availability of microbial genome sequences, is opening up data sources of unprecedented scale for drug discovery. Furthermore, the elucidation of genome expression states through transcriptomic and proteomic techniques is playing a crucial role in the characterisation of disease at the molecular level. At the same time, our still very limited knowledge of the biological functions of genes and proteins at different levels of cellular organisation is preventing full exploitation of the available data. This review will discuss current computational techniques for function prediction based on the sequence-structure-function paradigm. Newly emerging approaches aimed at gaining an expanded understanding of function through integration of data from various sources and modelling of complex cellular systems will also be highlighted.

Amino Acid Sequence↗
PubMed2003

JXP4BIGI: a generalized, Java XML-based approach for biological information gathering and integration.

MOTIVATION: In the post-genomic era, biologists interested in systems biology often need to import data from public databases and construct their own system-specific or subject-oriented databases to support their complex analysis and knowledge discovery. To facilitate the analysis and data processing, customized and centralized databases are often created by extracting and integrating heterogeneous data retrieved from public databases. A generalized methodology for accessing, extracting, transforming and integrating the heterogeneous data is needed. RESULTS: This paper presents a new data integration approach named JXP4BIGI (Java XML Page for Biological Information Gathering and Integration). The approach provides a system-independent framework, which generalizes and streamlines the steps of accessing, extracting, transforming and integrating the data retrieved from heterogeneous data sources to build a customized data warehouse. It allows the data integrator of a biological database to define the desired bio-entities in XML templates (or Java XML pages), and use embedded extended SQL statements to extract structured, semi-structured and unstructured data from public databases. By running the templates in the JXP4BIGI framework and using a number of generalized wrappers, the required data from public databases can be efficiently extracted and integrated to construct the bio-entities in the XML format without having to hard-code the extraction logics for different data sources. The constructed XML bio-entities can then be imported into either a relational database system or a native XML database system to build a biological data warehouse. AVAILABILITY: JXP4BIGI has been integrated and tested in conjunction with the IKBAR system (http://www.ikbar.org/) in two integration efforts to collect and integrate data for about 200 human genes related to cell death from HUGO, Ensembl, and SWISS-PROT (Bairoch and Apweiler, 2000), and about 700 Drosophila genes from FlyBase (FlyBase Consortium, 2002). The integrated data has been used in comparative genomic analysis of x-ray induced cell death. Also, as explained later, JXP4BIGI is a middleware and framework to be integrated with biological database applications, and cannot run as a stand-alone software for end users. For demonstration purposes, a demonstration version is accessible at (http://www.ikbar.org/jxp4bigi/demo.html).

Database Management Systems↗