FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps.
(73/1414)
MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer. (+info)
Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms.
(74/1414)
MOTIVATION: Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed. RESULTS: In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. AVAILABILITY: The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. SUPPLEMENTARY MATERIAL: Bioinformatics Online. (+info)
Hypothalamic digoxin and regulation of body mass index.
(75/1414)
The hypothalamus produces digoxin, an endogenous membrane Na+-K+ ATPase inhibitor and regulator of neurotransmission. Digoxin being a steroidal glycoside, is synthesised by the isoprenoid pathway. In view of the reports of elevated digoxin levels in metabolic syndrome X with high body mass index, the isoprenoid pathway mediated biochemical cascade was assessed in individuals with high and low body mass index. It was also assessed in individuals with differing hemispheric dominance to find out the relationship between digoxin status, body mass index and hemispheric dominance. The isoprenoid pathway metabolites, tryptophan / tyrosine catabolic patterns and membrane composition were assessed. In individuals with high body mass index an upregulated isoprenoid pathway with increased HMG CoA reductase activity, serum digoxin and dolichol levels and low ubiquinone levels were observed. The RBC membrane Na+-K+ ATPase activity and serum magnesium levels were decreased. The tyrosine catabolites (dopamine, morphine, epinephrine and norepinephrine) were reduced and the tryptophan catabolites (serotonin, quinolinic acid, strychnine and nicotine) were increased. There was an increase in membrane cholesterol : phospholipid ratio and a reduction in membrane glycoconjugates in individuals with high body mass index. The reverse patterns were seen in individuals with low body mass index. The patterns in individuals with high body mass index and low body mass index correlated with right hemispheric dominance and left hemispheric dominance respectively. Hemispheric dominance and digoxin status regulates the differential metabolic pattern observed in individuals with high and low body mass index. (+info)
Deciphering metabolic networks.
(76/1414)
All higher organisms divide major biochemical steps into different cellular compartments and often use tissue-specific division of metabolism for the same purpose. Such spatial resolution is accompanied with temporal changes of metabolite synthesis in response to environmental stimuli or developmental needs. Although analyses of primary and secondary gene products, i.e. transcripts, proteins, and metabolites, regularly do not cope with this spatial and temporal resolution, these gene products are often observed to be highly coregulated forming complex networks. Methods to study such networks are reviewed with respect to data acquisition, network statistics, and biochemical interpretation. (+info)
Fluctuations and slow variables in genetic networks.
(77/1414)
Computer simulations of large genetic networks are often extremely time consuming because, in addition to the biologically interesting translation and transcription reactions, many less interesting reactions like DNA binding and dimerizations have to be simulated. It is desirable to use the fact that the latter occur on much faster timescales than the former to eliminate the fast and uninteresting reactions and to obtain effective models of the slow reactions only. We use three examples of self-regulatory networks to show that the usual reduction methods where one obtains a system of equations of the Hill type fail to capture the fluctuations that these networks exhibit due to the small number of molecules; moreover, they may even miss describing the behavior of the average number of proteins. We identify the inclusion of fast-varying variables in the effective description as the cause for the failure of the traditional schemes. We suggest a different effective description, which entails the introduction of an additional species, not present in the original networks, that is slowly varying. We show that this description allows for a very efficient simulation of the reduced system while retaining the correct fluctuations and behavior of the full system. This approach ought to be applicable to a wide range of genetic networks. (+info)
The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.
(78/1414)
MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/ (+info)
Subnetwork hierarchies of biochemical pathways.
(79/1414)
MOTIVATION: The vastness and complexity of the biochemical networks that have been mapped out by modern genomics calls for decomposition into subnetworks. Such networks can have inherent non-local features that require the global structure to be taken into account in the decomposition procedure. Furthermore, basic questions such as to what extent the network (graph theoretically) can be said to be built by distinct subnetworks are little studied. RESULTS: We present a method to decompose biochemical networks into subnetworks based on the global geometry of the network. This method enables us to analyze the full hierarchical organization of biochemical networks and is applied to 43 organisms from the WIT database. Two types of biochemical networks are considered: metabolic networks and whole-cellular networks (also including for example information processes). Conceptual and quantitative ways of describing the hierarchical ordering are discussed. The general picture of the metabolic networks arising from our study is that of a few core-clusters centred around the most highly connected substances enclosed by other substances in outer shells, and a few other well-defined subnetworks. AVAILABILITY: An implementation of our algorithm and other programs for analyzing the data is available from http://www.tp.umu.se/forskning/networks/meta/ SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.tp.umu.se/forskning/networks/meta/ (+info)
Differentiation of stress, metabolism, communication, and defense responses following transplantation.
(80/1414)
The biological complexity of allograft rejection and alloantigen-independent mechanisms is poorly understood. Therefore, we analyzed four components of the biological response following transplantation by global gene analysis. A comparative and kinetic approach was used to identify gene expression profiles. Biological processes were assigned to genes displaying the largest alterations in expression. Metabolism, stress response, and cell organization were the predominant, biological processes associated with ischemia and systemic stress. Innate and adaptive immune responses induced a transcriptional shift toward defense and cell communication. The kinetic analysis showed a shift from innate toward adaptive responses in the post-transplant course. (+info)