Dirk Koschützki

Exploration of biological network centralities with CentiBiN

BackgroundThe elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability.ResultsCentiBiN (Cent ralities i n Bi ological N etworks) is a tool for the computation and exploration of centralities in biological networks such as protein-protein interaction networks. It computes 17 different centralities for directed or undirected networks, ranging from local measures, that is, measures that only consider the direct neighbourhood of a network element, to global measures. CentiBiN supports the exploration of the centrality distribution by visualising central elements within the network and provides several layout mechanisms for the automatic generation of graphical representations of a network. It supports different input formats, especially for biological networks, and the export of the computed centralities to other tools.ConclusionCentiBiN helps systems biology researchers to identify crucial elements of biological networks. CentiBiN including a user guide and example data sets is available free of charge at http://centibin.ipk-gatersleben.de/. CentiBiN is available in two different versions: a Java Web Start application and an installable Windows application.

Flux Balance Analysis of Barley Seeds: A Computational Approach to Study Systemic Properties of Central Metabolism

The accumulation of storage compounds is an important aspect of cereal seed metabolism. Due to the agronomical importance of the storage reserves of starch, protein, and oil, the understanding of storage metabolism is of scientific interest, with practical applications in agronomy and plant breeding. To get insight into storage patterning in developing cereal seed in response to environmental and genetic perturbation, a computational analysis of seed metabolism was performed. A metabolic network of primary metabolism in the developing endosperm of barley (Hordeum vulgare), a model plant for temperate cereals, was constructed that includes 257 biochemical and transport reactions across four different compartments. The model was subjected to flux balance analysis to study grain yield and metabolic flux distributions in response to oxygen depletion and enzyme deletion. In general, the simulation results were found to be in good agreement with the main biochemical properties of barley seed storage metabolism. The predicted growth rate and the active metabolic pathway patterns under anoxic, hypoxic, and aerobic conditions predicted by the model were in accordance with published experimental results. In addition, the model predictions gave insight into the potential role of inorganic pyrophosphate metabolism to maintain seed metabolism under oxygen deprivation.

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.

How to identify essential genes from molecular networks

BackgroundThe 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.ResultsBy simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, 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.ConclusionThe 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.

MetaCrop: a detailed database of crop plant metabolism

MetaCrop is a manually curated repository of high quality information concerning the metabolism of crop plants. This includes pathway diagrams, reactions, locations, transport processes, reaction kinetics, taxonomy and literature. MetaCrop provides detailed information on six major crop plants with high agronomical importance and initial information about several other plants. The web interface supports an easy exploration of the information from overview pathways to single reactions and therefore helps users to understand the metabolism of crop plants. It also allows model creation and automatic data export for detailed models of metabolic pathways therefore supporting systems biology approaches. The MetaCrop database is accessible at http://metacrop.ipk-gatersleben.de.

GEOMI: GEOmetry for maximum insight

This paper describes the GEOMI system, a visual analysis tool for the visualisation and analysis of large and complex networks. GEOMI provides a collection of network analysis methods, graph layout algorithms and several graph navigation and interaction methods. GEOMI is part of a new generation of visual analysis tools combining graph visualisation techniques with network analysis methods. GEOMI is available from http://www.cs.usyd.edu.au/~visual/valacon/geomi/.

Meta-All: a system for managing metabolic pathway information

BackgroundMany 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.ResultsWe 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.ConclusionMETA-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 http://bic-gh.de/meta-all and can be downloaded free of charge and installed locally.

DFA&OPT-METAFrame: A Tool Kit for Program Analysis and Optimazation

Whereas the construction process of a compiler for the early and late phases like syntactic analysis and code generation is well-supported by powerful tools, the optimizer, the key component for achieving highly efficient code is usually still hand-coded. The tool kit presented here supports this essential step in the construction of a compiler. The two key features making it exceptional are (1) that it automatically generates global program analyses for intraprocedural, interprocedural and parallel data flow problems, and (2) that it supports the combination of the results obtained to program optimizations.

Visual Analysis of Overlapping Biological Networks

This paper investigates a new problem of visualizing a set of overlapping networks. We present two methods for constructing visualization of two and three overlapping networks in three dimensions. Our methods aim to achieve both drawing aesthetics (or conventions) for each individual network and exposing the common nodes between the overlapping networks. We evaluated our approaches using biological networks including protein interaction network, metabolic network, and gene regulatory network, from the bacterium Escherichia coli and crop plants to demonstrate their usefulness to support biological analysis.

Basic-Block Graphs: Living Dinosaurs?

Since decades, basic-block (BB) graphs have been the state- of-the-art means for representing programs in advanced industrial compiler environments. The usual justification for introducing the intermediate BB-structures in the program representation is performance: analyses on BB-graphs are generally assumed to outperform their counterparts on single-instruction (SI) graphs, which, undoubtedly, are conceptually much simpler, easier to implement, and more straightforward to verify. In this article, we discuss the difference between the two program representations and show by means of runtime measurements that, according to the new computer generations, performance is no longer on the side of the more complex BB-graphs. In fact, it turns out that no sensible reason for the BB-structure remains. Rather, we will demonstrate that edge-labeled SI-graphs, which model statements in their edges instead of in their nodes as classical flow graphs do, are most adequate, both for the theoretical reasoning about and for the implementation of analysis and optimization algorithms. We are convinced that this perception has far-reaching consequences for the design of compiler systems.