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Publications (43) View all
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Article: Evolutionary significance of metabolic network properties.
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ABSTRACT: Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.Journal of The Royal Society Interface 11/2011; 9(71):1168-76. · 4.40 Impact Factor -
Article: Systems approaches to modelling pathways and networks.
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ABSTRACT: It has become commonly accepted that systems approaches to biology are of outstanding importance to gain understanding from the vast amount of data which is presently being generated by advancing high-throughput technologies. The diversity of methods to model pathways and networks has significantly expanded over the past two decades. Modern and traditional approaches are equally important and recent activities aim at integrating the advantages of both. While traditional methods, based on differential equations, are useful to study the dynamics of small systems, modern constraint-based models can be applied to genome-scale systems, but are not able to capture dynamic features. Integrating different approaches is important to develop consistent theoretical descriptions encompassing various scales of biological information. The rapid progress of the field of theoretical systems biology, however, demonstrates how our fundamental theoretical understanding of biology is gaining momentum. The scientific community has apparently accepted the challenge to truly understand the principles of life.Briefings in functional genomics 09/2011; 10(5):266-79. · 4.13 Impact Factor -
Article: Mass-balanced randomization of metabolic networks.
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ABSTRACT: MOTIVATION: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. RESULTS: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties.Bioinformatics 03/2011; 27(10):1397-403. · 5.47 Impact Factor -
Article: Carbohydrate-active enzymes exemplify entropic principles in metabolism.
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ABSTRACT: Glycans comprise ubiquitous and essential biopolymers, which usually occur as highly diverse mixtures. The myriad different structures are generated by a limited number of carbohydrate-active enzymes (CAZymes), which are unusual in that they catalyze multiple reactions by being relatively unspecific with respect to substrate size. Existing experimental and theoretical descriptions of CAZyme-mediated reaction systems neither comprehensively explain observed action patterns nor suggest biological functions of polydisperse pools in metabolism. Here, we overcome these limitations with a novel theoretical description of this important class of biological systems in which the mixing entropy of polydisperse pools emerges as an important system variable. In vitro assays of three CAZymes essential for central carbon metabolism confirm the power of our approach to predict equilibrium distributions and non-equilibrium dynamics. A computational study of the turnover of the soluble heteroglycan pool exemplifies how entropy-driven reactions establish a metabolic buffer in vivo that attenuates fluctuations in carbohydrate availability. We argue that this interplay between energy- and entropy-driven processes represents an important regulatory design principle of metabolic systems.Molecular Systems Biology 01/2011; 7:542. · 8.63 Impact Factor -
Article: Integration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks.
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ABSTRACT: The integrated analysis of different omics-level data sets is most naturally performed in the context of common process or pathway association. In this chapter, the two basic approaches for a metabolic pathway-centric integration of proteomics and metabolomics data are described: the knowledge-based approach relying on existing metabolic pathway information, and a data-driven approach that aims to deduce functional (pathway) associations directly from the data. Relevant algorithmic approaches for the generation of metabolic networks of model organisms, their functional analysis, database resources, visualization and analysis tools will be described. The use of proteomics data in the process of metabolic network reconstruction will be discussed.Methods in molecular biology (Clifton, N.J.) 01/2011; 694:341-63.
