Marc-Thorsten Hütt

Jacobs University, Bremen, Bremen, Germany

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Publications (59)134.59 Total impact

  • Yan Hao, Dieter Armbruster, Marc-Thorsten Hütt
    PLoS ONE 05/2015; 10(5):e0125467. DOI:10.1371/journal.pone.0125467 · 3.53 Impact Factor
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    ABSTRACT: The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible -excited -refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.
    Scientific Reports 01/2015; 5. DOI:10.1038/srep07870 · 5.08 Impact Factor
  • Simon Möller, Heike Hameister, Marc-Thorsten Hütt
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    ABSTRACT: Genome signatures are statistical properties of DNA sequences that provide information on the underlying species. It is not understood, how such species-discriminating statistical properties arise from processes of genome evolution and from functional properties of the DNA. Investigating the interplay of different genome signatures can contribute to this understanding. Here we analyze the statistical dependences of two such genome signatures: word frequencies and symbol correlations at short and intermediate distances. We formulate a statistical model of word frequencies in DNA sequences based on the observed symbol correlations and show that deviations of word counts from this correlation-based null model serve as a new genome signature. This signature (i) performs better in sorting DNA sequence segments according to their species origin and (ii) reveals unexpected species differences in the composition of microsatellites, an important class of repetitive DNA. While the first observation is a typical task in metagenomics projects and therefore an important benchmark for a genome signature, the latter suggests strong species differences in the biological mechanisms of genome evolution. On a more general level, our results highlight that the choice of null model (here: word abundances computed via symbol correlations rather than shorter word counts) substantially affects the interpretation of such statistical signals.
    Physica A: Statistical Mechanics and its Applications 11/2014; 414:216–226. DOI:10.1016/j.physa.2014.07.048 · 1.72 Impact Factor
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    ABSTRACT: Topological cycles in excitable networks can play an important role in maintaining the network activity. When properly activated, cycles act as dynamic pacemakers, sustaining the activity of the whole network. Most previous research has focused on the contributions of short cycles to network dynamics. Here, we identify the specific cycles that are used during different runs of activation in sparse random graphs, as a basis of characterizing the contribution of cycles of any length. Both simulation and a refined mean-field approach evidence a decrease in the cycle usage when the cycle length increases, reflecting a trade-off between long time for recovery after excitation and low vulnerability to out-of-phase external excitations. In spite of this statistical observation, we find that the successful usage of long cycles, though rare, has important functional consequences for sustaining network activity: The average cycle length is the main feature of the cycle length distribution that affects the average lifetime of activity in the network. Particularly, use of long, rather than short, cycles correlates with higher lifetime, and cutting shortcuts in long cycles tends to increase the average lifetime of the activity. Our findings, thus, emphasize the essential, previously underrated role of long cycles in sustaining network activity. On a more general level, the findings underline the importance of network topology, particularly cycle structure, for self-sustained network dynamics.
    Physical Review E 11/2014; 90(5):052805. DOI:10.1103/PhysRevE.90.052805 · 2.33 Impact Factor
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    Marc-Thorsten Hütt, Marcus Kaiser, Claus C Hilgetag
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    ABSTRACT: The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.
    Philosophical Transactions of The Royal Society B Biological Sciences 10/2014; 369(1653). DOI:10.1098/rstb.2013.0522 · 6.31 Impact Factor
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    David F Klosik, Stefan Bornholdt, Marc-Thorsten Hütt
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    ABSTRACT: Following the work of Krumov et al. [Eur. Phys. J. B 84, 535 (2011)] we revisit the question whether the usage of large citation datasets allows for the quantitative assessment of social (by means of coauthorship of publications) influence on the progression of science. Applying a more comprehensive and well-curated dataset containing the publications in the journals of the American Physical Society during the whole 20th century we find that the measure chosen in the original study, a score based on small induced subgraphs, has to be used with caution, since the obtained results are highly sensitive to the exact implementation of the author disambiguation task.
    Physical Review E 09/2014; 90(3-1):032811. DOI:10.1103/PhysRevE.90.032811 · 2.33 Impact Factor
  • Marc-Thorsten Hütt
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    ABSTRACT: Pharmacology is currently transformed by the vast amounts of genome-associated information available for system-level interpretation. Here I review the potential of systems biology to facilitate this interpretation, thus paving the way for the emerging field of systems pharmacology. In particular, I will show how gene regulatory and metabolic networks can serve as a framework for interpreting high throughput data and as an interface to detailed dynamical models. In addition to the established connectivity analyses of effective networks, I suggest here to also analyze higher order architectural properties of effective networks.
    British Journal of Clinical Pharmacology 04/2014; 77(4):597-605. DOI:10.1111/bcp.12266 · 3.69 Impact Factor
  • Miriam Grace, Marc-Thorsten Hütt
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    ABSTRACT: Diversity-induced resonance, the emergence of coherent spatiotemporal patterns at intermediate parameter disorder, is a well-known phenomenon in lattices of excitable elements. Here we study the pattern events behind diversity-induced resonance in a lattice of coupled FitzHugh-Nagumo oscillators. Starting out with the observation that maximal spiral wave counts occur at intermediate values of parameter diversity, we analyze the competition between spiral and target wave patterns in the asymptotic collective state. We devise stylized numerical “in silico” competition experiments of (individual) patterns to understand the regulating parameters of the competing pattern events occurring stochastically in the full (“in vivo”) numerical simulation. Our analysis shows that pattern competition is a principal driving mechanism behind this form of diversity-induced resonance and that different types of competition take place: some follow the frequency composition of target and spiral waves, others are dictated by the statistics of parameter distributions. In particular, the increase and decrease of spiral wave counts with increasing diversity are statistically regulated by the number of oscillatory elements in the lattice, rather than by the frequency differences between target and spiral waves.
    Physics of Condensed Matter 02/2014; 87(2). DOI:10.1140/epjb/e2013-40873-8 · 1.46 Impact Factor
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    Claus C Hilgetag, Marc-Thorsten Hütt
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    ABSTRACT: A new paper shows that a characteristic feature of the arrangement of brain networks, their modular organization across several scales, is responsible for an expanded range of critical neural dynamics. This finding solves several puzzles in computational neuroscience and links fundamental aspects of neural network organization and brain dynamics.
    Trends in Cognitive Sciences 11/2013; 18(3). DOI:10.1016/j.tics.2013.10.016 · 21.15 Impact Factor
  • Moritz Emanuel Beber, Dieter Armbruster, Marc-Thorsten Hütt
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    ABSTRACT: Modules are common functional and structural properties of many social, technical and biological networks. Especially for biological systems it is important to understand how modularity is related to function and how modularity evolves. It is known that time-varying or spatially organized goals can lead to modularity in a simulated evolution of signaling networks. Here, we study a minimal model of material flow in networks. We discuss the relation between the shared use of nodes, i.e., the cooperativity of modules, and the orthogonality of a prescribed output pattern. We study the persistence of cooperativity through an evolution of robustness against local damages. We expect the results to be valid for a large class of flow-based biological and technical networks. Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2013-40672-3
    Physics of Condensed Matter 11/2013; 86(11):473-. DOI:10.1140/epjb/e2013-40672-3 · 1.46 Impact Factor
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    Miriam Grace, Marc-Thorsten Hütt
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    ABSTRACT: In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a 'developmental path'), together with differences in cell properties ('desynchronization' of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh-Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or 'spread') of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system.
    Journal of The Royal Society Interface 01/2013; 10(81):20121016. DOI:10.1098/rsif.2012.1016 · 3.86 Impact Factor
  • Moritz Emanuel Beber, Marc-Thorsten Hütt
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    ABSTRACT: Metabolism is a fascinating natural production and distribution process. Metabolic systems can be represented as a layered network, where the input layer consists of all the nutrients in the environment (raw materials entering the production process in the cell), subsequently to be processed by a complex network of biochemical reactions (middle layer) and leading to a well-defined output pattern, optimizing, for example, cell growth. Mathematical frameworks exploiting this layered-network representation of metabolism allow the prediction of metabolic fluxes (the cell’s ‘material flow’) under diverse conditions. In combination with suitable minimal models, it is possible to identify fundamental design principles and understand the efficiency and robustness of metabolic systems. Here, we summarize some design principles of metabolic systems from the perspective of production logistics and explore, how these principles can serve as templates for the design of robust manufacturing systems.
    Logistics Research 11/2012; 5(3-4). DOI:10.1007/s12159-012-0090-0
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    Nikolaus Sonnenschein, Carsten Marr, Marc-Thorsten Hütt
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    ABSTRACT: Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.
    09/2012; 2(3):632-647. DOI:10.3390/metabo2030632
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    ABSTRACT: Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.
    Journal of The Royal Society Interface 08/2012; 9(77):3426-35. DOI:10.1098/rsif.2012.0490 · 3.86 Impact Factor
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    ABSTRACT: Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.
    Frontiers in Computational Neuroscience 07/2012; 6:50. DOI:10.3389/fncom.2012.00050 · 2.23 Impact Factor
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    ABSTRACT: The pattern of over- and under-representations of three-node subgraphs has become a standard method of characterizing complex networks and evaluating how this intermediate level of organization contributes to network function. Understanding statistical properties of subgraph counts in random graphs, their fluctuations, and their interdependences with other topological attributes is an important prerequisite for such investigations. Here we introduce a formalism for predicting subgraph fluctuations induced by perturbations of unidirectional and bidirectional edge densities. On this basis we predict the over-and under-representation of subgraphs arising from a density mismatch between a network and the corresponding pool of randomized graphs serving as a null model. Such mismatches occur, for example, in modular and hierarchical graphs.
    Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics 05/2012; 85(5):056119. DOI:10.1103/PhysRevE.85.056119 · 2.33 Impact Factor
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    ABSTRACT: Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.
    BMC Systems Biology 05/2012; 6(1):41. DOI:10.1186/1752-0509-6-41 · 2.85 Impact Factor
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    ABSTRACT: How signals propagate through a network as a function of the network architecture and under the influence of noise is a fundamental question in a broad range of areas dealing with signal processing - from neuroscience to electrical engineering and communication technology. Here we use numerical simulations and a mean-field approach to analyze a minimal dynamic model for signal propagation. By labeling and tracking the excitations propagating from a single input node to remote output nodes in random networks, we show that noise (provided by spontaneous node excitations) can lead to an enhanced signal propagation, with a peak in the signal-to-noise ratio at intermediate noise intensities. This network analog of stochastic resonance is not captured by a mean-field description that incorporates topology only on the level of the average degree, indicating that the detailed network topology plays a significant role in signal propagation.Highlights► We explore the propagation of excitations through a network under the influence of noise. ► Special emphasis is on the application to neuroscience. ► A novel labeling technique of signal excitations is introduced and compared to the classical signal-to-noise ratio. ► We show that noise can lead to an enhanced signal propagation, with a peak in the signal-to-noise ratio at intermediate noise intensities.
    Chaos Solitons & Fractals 05/2012; 45(5):611-618. DOI:10.1016/j.chaos.2011.12.011 · 1.50 Impact Factor
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    ABSTRACT: Recent publications on traffic control in urban road networks have presented strategies for adaptive flow control under varying network load. Traffic lights are modeled as independent, periodic devices, which take decisions on a local level. Despite the not actively coordinated decisions at single network nodes, such a strategy can perform better in terms of waiting time for the whole system than applying a standard optimization approach. First results from an ongoing simulation study indicate that adaptive control algorithms can also be applied successfully to different network topologies, e.g., logistic networks. This article presents the outcome of an extended simulation study. The simulation experiments have been carried out on artificially generated networks as well as networks derived from real manufacturing environments. We are able to show that the promising findings from traffic control regarding waiting time reduction and the emergence of synchronized behavior can be reproduced for production logistics. Furthermore, we illustrate how the variance of the network degree as an indicator for network connectivity influences the logistic performance.
    CIRP Journal of Manufacturing Science and Technology 01/2012; 5(4):309–318. DOI:10.1016/j.cirpj.2012.09.004
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    ABSTRACT: Both metabolic and manufacturing systems face fluctuating environmental influences and thus share the common challenge to maintain a high level of efficiency for a variety of different conditions. Therefore, transferring methods used for analyzing one of the systems can lead to gaining new insights in the other. Following-up on previous findings on analogies in metabolic and manufacturing systems, our approach now is to analyze and compare complex network measures such as centrality or flow activity in both systems to identify quantified relations. The results show that both systems also display distinct statistical differences in addition to their various structural similarities.
    Dynamics in Logistics. Third International Conference, LDIC 2012; 01/2012