How to identify essential genes from molecular networks?

<p>Abstract</p> <p>Background</p> <p>The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.</p> <p>Results</p> <p>By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for <it>Saccharomyces cerevisiae</it>, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined.</p> <p>Conclusion</p> <p>The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.</p

Meta-All: a system for managing metabolic pathway information

BACKGROUND: Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed. RESULTS: We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. CONCLUSION: META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at and can be downloaded free of charge and installed locally

Comparison of Centralities for Biological Networks

The analysis of biological networks involves the evaluation of the vertices within the connection structure of the network. To support this analysis we discuss five centrality measures and demonstrate their applicability on two example networks, a protein-protein-interaction network and a transcriptional regulation network. We show that all five centrality measures result in different valuations of the vertices and that for the analysis of biological networks all five measures are of interest

Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks

The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.publishe

Graph Pattern Analysis with PatternGravisto

The analysis of patterns in graphs has applications in many fields of science. We propose a new method for analyzing graph patterns consisting of a user-friendly and flexible mechanism to specify patterns, an algorithm to recognize multiple appearances of patterns in a target graph, a pattern preserving layout algorithm, and a navigation technique to explore the underlying structure of the graph given by the patterns. This method has been implemented in a tool called PatternGravisto. We demonstrate the utility of our approach with the example graphs from the Graph Drawing Contest 2003 which cover problems from biology and sociology

Kinetic Modelling with the Systems Biology Modelling Environment SyBME

Kinetic modelling and simulation is an important approach in systems biology. While the focus of current modelling tools is on simulation, model development is a highly iterative process which is currently only partly supported. To support the development of biochemical models, their simulation, and graphical understanding, we designed and implemented SyBME, the Systems Biology Modelling Environment. Here we present the architecture and the main components of SyBME and show its use by modelling sucrose breakdown in developing potato tubers