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Search Results: 1 - 10 of 154047 matches for " Oliver Ebenh?h "
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Ground State Robustness as an Evolutionary Design Principle in Signaling Networks
?nder Kartal,Oliver Ebenhh
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0008001
Abstract: The ability of an organism to survive depends on its capability to adapt to external conditions. In addition to metabolic versatility and efficient replication, reliable signal transduction is essential. As signaling systems are under permanent evolutionary pressure one may assume that their structure reflects certain functional properties. However, despite promising theoretical studies in recent years, the selective forces which shape signaling network topologies in general remain unclear. Here, we propose prevention of autoactivation as one possible evolutionary design principle. A generic framework for continuous kinetic models is used to derive topological implications of demanding a dynamically stable ground state in signaling systems. To this end graph theoretical methods are applied. The index of the underlying digraph is shown to be a key topological property which determines the so-called kinetic ground state (or off-state) robustness. The kinetic robustness depends solely on the composition of the subdigraph with the strongly connected components, which comprise all positive feedbacks in the network. The component with the highest index in the feedback family is shown to dominate the kinetic robustness of the whole network, whereas relative size and girth of these motifs are emphasized as important determinants of the component index. Moreover, depending on topological features, the maintenance of robustness differs when networks are faced with structural perturbations. This structural off-state robustness, defined as the average kinetic robustness of a network's neighborhood, turns out to be useful since some structural features are neutral towards kinetic robustness, but show up to be supporting against structural perturbations. Among these are a low connectivity, a high divergence and a low path sum. All results are tested against real signaling networks obtained from databases. The analysis suggests that ground state robustness may serve as a rationale for some structural peculiarities found in intracellular signaling networks.
Functional Classification of Genome-Scale Metabolic Networks
Oliver Ebenhh, Thomas Handorf
EURASIP Journal on Bioinformatics and Systems Biology , 2009, DOI: 10.1155/2009/570456
Abstract: Genome-scale metabolic networks ideally comprise all enzymatic reactions that occur inside the cells of a specific organism. With the ever increasing number of fully sequenced genomes (at present, over 700 genome sequences have been published and well over 2000 sequencing projects are ongoing, [1]) and the advent of biochemical databases such as KEGG [2] or MetaCyc [3] in which the knowledge about the enzymes encoded in the genomes is compactly stored, organism-wide metabolic networks have now become easily accessible for a considerable number of species.Whereas such models usually contain quite accurate information on the stoichiometry, that is the wiring, of the network, detailed knowledge on the kinetic properties of the enzymes catalyzing the involved reactions is still sparse. In the recent years, a number of analysis techniques have emerged which account for this fact and require only information about the stoichiometries of the participating reactions. A particularly useful framework is that of flux balance analysis which allows to infer optimal flux distributions given the structure of the network and an output function which is to be optimized. For the network of E. coli, for example, this approach has successfully been applied to predict flux distributions under the premise that biomass accumulation is maximized [4]. Further, in many cases, flux distributions could successfully be predicted for knock-out mutants lacking a particular enzyme [5].In the recent past, we have proposed a complementary strategy for the analysis of large-scale metabolic networks, the so-called method of network expansion [6]. In this approach, networks of increasing size are constructed starting from an initial set of substrates (the seed) by stepwise adding all those reactions from the analyzed metabolic network, which use as substrates only compounds present in the seed or provided as products by reactions incorporated in earlier steps. The set of metabolites contained in the fi
Functional Classification of Genome-Scale Metabolic Networks
Ebenhh Oliver,Handorf Thomas
EURASIP Journal on Bioinformatics and Systems Biology , 2009,
Abstract: We propose two strategies to characterize organisms with respect to their metabolic capabilities. The first, investigative, strategy describes metabolic networks in terms of their capability to utilize different carbon sources, resulting in the concept of carbon utilization spectra. In the second, predictive, approach minimal nutrient combinations are predicted from the structure of the metabolic networks, resulting in a characteristic nutrient profile. Both strategies allow for a quantification of functional properties of metabolic networks, allowing to identify groups of organisms with similar functions. We investigate whether the functional description reflects the typical environments of the corresponding organisms by dividing all species into disjoint groups based on whether they are aerotolerant and/or photosynthetic. Despite differences in the underlying concepts, both measures display some common features. Closely related organisms often display a similar functional behavior and in both cases the functional measures appear to correlate with the considered classes of environments. Carbon utilization spectra and nutrient profiles are complementary approaches toward a functional classification of organism-wide metabolic networks. Both approaches contain different information and thus yield different clusterings, which are both different from the classical taxonomy of organisms. Our results indicate that a sophisticated combination of our approaches will allow for a quantitative description reflecting the lifestyles of organisms.
Biosynthetic Potentials of Metabolites and Their Hierarchical Organization
Franziska Matth?us ,Carlos Salazar ,Oliver Ebenhh
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.1000049
Abstract: A major challenge in systems biology is to understand how complex and highly connected metabolic networks are organized. The structure of these networks is investigated here by identifying sets of metabolites that have a similar biosynthetic potential. We measure the biosynthetic potential of a particular compound by determining all metabolites than can be produced from it and, following a terminology introduced previously, call this set the scope of the compound. To identify groups of compounds with similar scopes, we apply a hierarchical clustering method. We find that compounds within the same cluster often display similar chemical structures and appear in the same metabolic pathway. For each cluster we define a consensus scope by determining a set of metabolites that is most similar to all scopes within the cluster. This allows for a generalization from scopes of single compounds to scopes of a chemical family. We observe that most of the resulting consensus scopes overlap or are fully contained in others, revealing a hierarchical ordering of metabolites according to their biosynthetic potential. Our investigations show that this hierarchy is not only determined by the chemical complexity of the metabolites, but also strongly by their biological function. As a general tendency, metabolites which are necessary for essential cellular processes exhibit a larger biosynthetic potential than those involved in secondary metabolism. A central result is that chemically very similar substances with different biological functions may differ significantly in their biosynthetic potentials. Our studies provide an important step towards understanding fundamental design principles of metabolic networks determined by the structural and functional complexity of metabolites.
Bioinformatics meets systems biology
Carlos Salazar, Jana Schütze, Oliver Ebenhh
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-1-303
Abstract: The efficient integration of bioinformatics and systems biology requires worldwide cooperation not only in the research of senior scientists but also in the research training of young scientists. To this end, a student-focused workshop on bioinformatics and systems biology http://www.biologie.hu-berlin.de/gk/ibsb2005 webcite was held last August at Humboldt University in Berlin, Germany. This was the fifth annual workshop held as part of a research collaboration between the Bioinformatics Program of Boston University in the USA, the Bioinformatics Center of Kyoto University in Japan, and the Berlin-located graduate program 'Dynamics and Evolution of Cellular and Macromolecular Processes'. This time the meeting had two main themes - the integration of genomic and chemical information in the analysis of the dynamics and topology of cellular regulatory networks, and the development of more accurate computational tools for the analysis of gene expression and the prediction of transcription-factor binding sites. Full papers accepted for the fifth workshop have been published in the Genome Informatics Series of the Japanese Society of Bioinformatics, edited by Satoru Miyano (University of Tokyo, Japan) http://www.jsbi.org/journal/GI16_1.html webcite.Trends in genome biology and bioinformatics were highlighted in the opening talk by Minoru Kanehisa (Kyoto University Bioinformatics Center, Japan), whose group is responsible for the Kyoto Encyclopedia of Genes and Genomes (KEGG) database http://www.genome.ad.jp/kegg webcite. This stores molecular interaction networks and graphics, including metabolic pathways, regulatory pathways and molecular complexes. Kanehisa emphasized the importance of an integrated analysis of genomic and chemical information to predict the complete functional behaviors of cells, organisms and ecosystems. While traditional genomics and other 'omics' have contributed to our knowledge of the genes and proteins that make up a biological system, new chemi
The Metabolic Interplay between Plants and Phytopathogens
Guangyou Duan,Nils Christian,Jens Schwachtje,Dirk Walther,Oliver Ebenhh
Metabolites , 2013, DOI: 10.3390/metabo3010001
Abstract: Plant diseases caused by pathogenic bacteria or fungi cause major economic damage every year and destroy crop yields that could feed millions of people. Only by a thorough understanding of the interaction between plants and phytopathogens can we hope to develop strategies to avoid or treat the outbreak of large-scale crop pests. Here, we studied the interaction of plant-pathogen pairs at the metabolic level. We selected five plant-pathogen pairs, for which both genomes were fully sequenced, and constructed the corresponding genome-scale metabolic networks. We present theoretical investigations of the metabolic interactions and quantify the positive and negative effects a network has on the other when combined into a single plant-pathogen pair network. Merged networks were examined for both the native plant-pathogen pairs as well as all other combinations. Our calculations indicate that the presence of the parasite metabolic networks reduce the ability of the plants to synthesize key biomass precursors. While the producibility of some precursors is reduced in all investigated pairs, others are only impaired in specific plant-pathogen pairs. Interestingly, we found that the specific effects on the host’s metabolism are largely dictated by the pathogen and not by the host plant. We provide graphical network maps for the native plant-pathogen pairs to allow for an interactive interrogation. By exemplifying a systematic reconstruction of metabolic network pairs for five pathogen-host pairs and by outlining various theoretical approaches to study the interaction of plants and phytopathogens on a biochemical level, we demonstrate the potential of investigating pathogen-host interactions from the perspective of interacting metabolic networks that will contribute to furthering our understanding of mechanisms underlying a successful invasion and subsequent establishment of a parasite into a plant host.
Mesoscopic behavior from microscopic Markov dynamics and its application to calcium release channels
Nils Christian,Alexander Skupin,Silvia Morante,Karl Jansen,Giancarlo Rossi,Oliver Ebenhh
Quantitative Biology , 2013, DOI: 10.1016/j.jtbi.2013.11.010
Abstract: A major challenge in biology is to understand how molecular processes determine phenotypic features. We address this fundamental problem in a class of model systems by developing a general mathematical framework that allows the calculation of mesoscopic properties from the knowledge of microscopic Markovian transition probabilities. We show how exact analytic formulae for the first and second moments of resident time distributions in mesostates can be derived from microscopic resident times and transition probabilities even for systems with a large number of microstates. We apply our formalism to models of the inositol trisphosphate receptor, which plays a key role in generating calcium signals triggering a wide variety of cellular responses. We demonstrate how experimentally accessible quantities, such as opening and closing times and the coefficient of variation of inter-spike intervals, and other, more elaborated, quantities can be analytically calculated from the underlying microscopic Markovian dynamics. A virtue of our approach is that we do not need to follow the detailed time evolution of the whole system, as we derive the relevant properties of its steady state without having to take into account the often extremely complicated transient features. We emphasize that our formulae fully agree with results obtained by stochastic simulations and approaches based on a full determination of the microscopic system's time evolution. We also illustrate how experiments can be devised to discriminate between alternative molecular models of the inositol trisphosphate receptor. The developed approach is applicable to any system described by a Markov process and, owing to the analytic nature of the resulting formulae, provides an easy way to characterize also rare events that are of particular importance to understand the intermittency properties of complex dynamic systems.
Instability of a Uniform Plankton Distribution
Wolfgang Ebenhh
Modeling, Identification and Control , 1981, DOI: 10.4173/mic.1981.2.3
Abstract: For a plankton model system, a horizontally uniform distribution becomes unstable if the zooplankton component carries out diurnal vertical migrations in an ocean with a speed difference between the currents in upper and lower waterlaycrs. With turbulent diffusion included in the model, the instability occurs beyond a threshold speed difference. A numerical estimation of the threshold and of the critical patch size gives reasonable values.
A Model of the Dynamics of Plankton Patchiness
Wolfgang Ebenhh
Modeling, Identification and Control , 1980, DOI: 10.4173/mic.1980.2.2
Abstract: A mathematical model of the dynamics of plankton patchiness in the intermediate scale (1 km-10 km) was developed. Mechanisms that may be important in the creation and destruction of patches were selected and modelled. Such mechanisms are: horizontal turbulent diffusion, noise in the vertical turbulence, vertical migration of the zooplankton combined with a velocity profile and consumption of zooplankton by fish in schools. Patchiness is described by thc usc of the moments of density distributions, coherence lengths and correlations of phytoplankton and zooplankton. These parameters are investigated as functions of time and, also, for their dependence on the parameters of the patch creation mechanisms.
Microarrays, deep sequencing and the true measure of the transcriptome
John H Malone, Brian Oliver
BMC Biology , 2011, DOI: 10.1186/1741-7007-9-34
Abstract: The transcriptome, the entire repertoire of transcripts in a species, represents a key link between information encoded in DNA and phenotype. A fully quantitatively described transcriptome is dauntingly large. For example, there are more than 3 billion bases in the human genome, about 1014 cells in the body, each cell has about 300,000 molecules of RNA [1], and the average gene size is about 28 kilobase pairs [2]. Thus, for a full representation of a human, there are about 8.423 (280000 × 300000 × 1014) RNA bases in the full transcriptome. The tools for profiling RNA have been available for years, as Northern blots, reverse-transcription PCR (RT-PCR), expressed sequence tags (ESTs), and serial analysis of gene expression (SAGE). But the rapid and high-throughput quantification of the transcriptome became a possibility only with the development of gene expression microarrays [3]. With the more recent advent of techniques for direct sequencing of the transcriptional output of the genome, we can now at least begin to think about a complete transcriptional characterization of all the cells of an organism.Gene expression microarray results have produced much important information about how the transcriptome is deployed in different cell types [4] and tissues [5], how gene expression changes across development states [6,7] and disease phenotypes [8,9], and how it varies within [10] and between species [11]. They have also led to surprising and contentious conclusions on how much of the genome is transcribed into non-coding RNAs.The starting point for a microarray is a set of short oligonucleotide probes representing genomic DNA. A typical modern microarray consists of patches of such probes complementary to the transcripts whose presence is to be investigated, and immobilized on a solid substrate. In modern arrays, probe design is usually based on genome sequence or on known or predicted open reading frames and usually multiple probes are designed per gene model. Transcri
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