3 items tagged with 'pathways'.
Abstract (Expand)
Enormous amounts of data result from genome sequencing projects and new experimental methods. Within this tremendous amount of genomic data 30-40 per cent of the genes being identified in an organism … remain unknown in terms of their biological function. As a consequence of this lack of information the overall schema of all the biological functions occurring in a specific organism cannot be properly represented. To understand the functional properties of the genomic data more experimental data must be collected. A pathway database is an effort to handle the current knowledge of biochemical pathways and in addition can be used for interpretation of sequence data. Some of the existing pathway databases can be interpreted as detailed functional annotations of genomes because they are tightly integrated with genomic information. However, experimental data are often lacking in these databases. This paper summarises a list of pathway databases and some of their corresponding biological databases, and also focuses on information about the content and the structure of these databases, the organisation of the data and the reliability of stored information from a biological point of view. Moreover, information about the representation of the pathway data and tools to work with the data are given. Advantages and disadvantages of the analysed databases are pointed out, and an overview to biological scientists on how to use these pathway databases is given.
Authors: U. Wittig, A. De Beuckelaer
Date Published: 24th Jul 2001
Publication Type: Journal
PubMed ID: 11465731
Citation: Brief Bioinform. 2001 May;2(2):126-42.
Created: 8th Nov 2017 at 10:45, Last updated: 5th Mar 2024 at 21:23
Abstract (Expand)
To provide support for the analysis of biochemical pathways a database system based on a model that represents the characteristics of the domain is needed. This domain has proven to be difficult to … model by using conventional data modelling techniques. We are building an ontology for biochemical pathways, which acts as the basis for the generation of a database on the same domain, allowing the definition of complex queries and complex data representation. The ontology is used as a modelling and analysis tool which allows the expression of complex semantics based on a first-order logic representation language. The induction capabilities of the system can help the scientist in formulating and testing research hypotheses that are difficult to express with the standard relational database mechanisms. An ontology representing the shared formalisation of the knowledge in a scientific domain can also be used as data integration tool clarifying the mapping of concepts to the developers of different databases. In this paper we describe the general structure of our system, concentrating on the ontology-based database as the key component of the system.
Authors: I. Rojas, L. Bernardi, E. Ratsch, R. Kania, U. Wittig, J. Saric
Date Published: 18th Jun 2002
Publication Type: Journal
PubMed ID: 12066842
Citation: In Silico Biol. 2002;2(2):75-86.
Created: 8th Nov 2017 at 10:47, Last updated: 5th Mar 2024 at 21:23
Abstract (Expand)
Transcription is one of the basic processes of gene expression, controlled by a complex network of biochemical reactions. Despite its importance, most work on the visualisation of biochemical networks … focuses on the representation of metabolic pathways. The visualisation of the complex networks controlling transcription requires the implementation of a hierarchical approach that allows the display of the structure of each regulatory region with its transcription factors and regulated operons. This paper presents a web-based application for the visualisation of transcriptional control networks. It takes as case study the organism Escherichia coli. The definition of the visual components implemented is mainly based on those proposed by Shen-Orr et al., 2002, slightly extended to visualise complex networks.
Authors: N. Sosa, A. Kremling, E. Ratsch, I. Rojas
Date Published: 28th Oct 2004
Publication Type: Journal
PubMed ID: 15506999
Citation: In Silico Biol. 2004;4(4):507-15.
Created: 12th Oct 2017 at 15:32, Last updated: 5th Mar 2024 at 21:23