[show abstract][hide abstract] 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; · 16.01 Impact Factor
[show abstract][hide abstract] 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. · 4.91 Impact Factor
[show abstract][hide abstract] 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. · 4.91 Impact Factor
[show abstract][hide abstract] 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. · 2.48 Impact Factor
[show abstract][hide abstract] 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. · 2.98 Impact Factor
[show abstract][hide abstract] 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.
[show abstract][hide abstract] ABSTRACT: The 3D structure of the chromosome of the model organism Escherichia coli is one key component of its gene regulatory machinery. This type of regulation mediated by topological transitions of the chromosomal DNA can be thought of as an analog control, complementing the digital control, i.e. the network of regulation mediated by dedicated transcription factors. It is known that alterations in the superhelical density of chromosomal DNA lead to a rich pattern of differential expressed genes. Using a network approach, we analyze these expression changes for wild type E. coli and mutants lacking nucleoid associated proteins (NAPs) from a metabolic and transcriptional regulatory network perspective.
We find a significantly higher correspondence between gene expression and metabolism for the wild type expression changes compared to mutants in NAPs, indicating that supercoiling induces meaningful metabolic adjustments. As soon as the underlying regulatory machinery is impeded (as for the NAP mutants), this coherence between expression changes and the metabolic network is substantially reduced. This effect is even more pronounced, when we compute a wild type metabolic flux distribution using flux balance analysis and restrict our analysis to active reactions. Furthermore, we are able to show that the regulatory control exhibited by DNA supercoiling is not mediated by the transcriptional regulatory network (TRN), as the consistency of the expression changes with the TRN logic of activation and suppression is strongly reduced in the wild type in comparison to the mutants.
So far, the rich patterns of gene expression changes induced by alterations of the superhelical density of chromosomal DNA have been difficult to interpret. Here we characterize the effective networks formed by supercoiling-induced gene expression changes mapped onto reconstructions of E. coli's metabolic and transcriptional regulatory network. Our results show that DNA supercoiling coordinates gene expression with metabolism. Furthermore, this control is acting directly because we can exclude the potential role of the TRN as a mediator.
BMC Systems Biology 03/2011; 5:40. · 2.98 Impact Factor
[show abstract][hide abstract] ABSTRACT: The general view of flow networks controlled by periodic devices provides a powerful unifying language for diverse fields of application. Recent publications on traffic control have presented strategies for adaptive flow control. For adapting this view to production logistics two improvements are necessary: A discussion of more general network architectures than in traffic and a generalization towards more sophisticated performance measures beyond waiting time. Our simulation study illustrates how strongly parameter variation influences the logistic performance and we show whether the promising findings from traffic control regarding waiting time reduction and the emergence of synchronized behavior can be reproduced for production logistics.
Proceedings of the 44th CIRP International Conference on Manufacturing Systems; 01/2011
[show abstract][hide abstract] ABSTRACT: Identifying causes of lateness in multistage production systems demands methods for considering a high-dimensional order and process attribute space. Simultaneous measurement of expression levels of thousands of genes in a biological cell provides a data set for understanding robust cellular function. Methods developed in computational systems biology for analyzing gene expression data enable the identification of the most influential criteria sets. Gene expression is the production process of functional elements (enzymes, proteins) in a biological cell. Logistics data analysis faces a similar challenge: What attributes of orders can be associated with high and low punctuality? We combine methods from cluster analysis and computational systems biology to explore the relationship between order and resource parameters and lateness. With this novel approach we determine intrinsic interdependencies between order parameters and process parameters. For the case study described here, this approach has improved the precision of predicting the lateness of an order by 14% compared to a majority vote among neighboring orders in parameter space.
[show abstract][hide abstract] ABSTRACT: Metabolic systems need to show high performance under typical environmental conditions and, at the same time, maintain certain functions under a broad range of perturbations and varying conditions. It is precisely this robustness with respect to large environmental changes that makes metabolic networks a potentially very interesting role model for technical production and distribution systems. Here we develop a formalism to compare these systems and show that optimization strategies from one domain can also be successfully applied to the other domains.
Journal of Statistical Mechanics Theory and Experiment 01/2011; 2011(05):P05004. · 1.87 Impact Factor
[show abstract][hide abstract] ABSTRACT: Timetable construction belongs to the most important optimization problems in public transport. Finding optimal or near-optimal timetables under the subsidiary conditions of minimizing travel times and other criteria is a targeted contribution to the functioning of public transport. In addition to efficiency (given, e.g., by minimal average travel times), a significant feature of a timetable is its robustness against delay propagation. Here we study the balance of efficiency and robustness in long-distance railway timetables (in particular the current long-distance railway timetable in Germany) from the perspective of synchronization, exploiting the fact that a major part of the trains run nearly periodically. We find that synchronization is highest at intermediate-sized stations. We argue that this synchronization perspective opens a new avenue towards an understanding of railway timetables by representing them as spatio-temporal phase patterns. Robustness and efficiency can then be viewed as properties of this phase pattern.
Physics of Condensed Matter 03/2010; · 1.28 Impact Factor
[show abstract][hide abstract] ABSTRACT: Statistical correlations in DNA sequences are an important source of information for processes of genome evolution. As a special case of such correlations and building up on our previous work, here we study, how short-range correlations in Eukaryotic genomes change under elimination of various classes of repetitive DNA. Our main result is that a residual correlation pattern, common to most mammalian species, emerges under elimination of all repetitive DNA, suggesting features of an ancestral correlation signature. Furthermore, using this general framework, we find classes of repeats, which upon deletion move the correlation pattern towards this residual pattern (simple repeats and SINEs) or away from this residual pattern (LINEs). These findings suggest that the common correlation pattern visible in the mammalian species after repeat elimination can be associated with a common mammalian ancestor.
Bio Systems 03/2010; 100(3):215-24. · 1.27 Impact Factor
[show abstract][hide abstract] ABSTRACT: Excitable media dynamics is the lossless active transmission of waves of excitation over a field of coupled elements, such as electrical excitation in heart tissue or nerve fibers, cAMP signaling in the slime mold Dictyostelium discoideum or waves of chemical activity in the Belousov–Zhabotinsky reaction. All these systems follow essentially the same generic dynamics, including undamped wave transmission and the self-organized emergence of circular target and self-sustaining spiral waves. We combine spiral recognition, using the established phase singularity technique, and a novel three-dimensional fitting algorithm for noise-resistant target wave recognition to extract all important events responsible for the layout of the asymptotic large-scale pattern. Space–time plots of these combined events reveal signatures of events leading to spiral formation, illuminating the microscopic mechanisms at work. This strategy can be applied to arbitrary excitable media data from either models or experiments, giving insight into for example the microscopic causes for formation of pathological spiral waves in heart tissue, which could lead to novel techniques for diagnosis, risk evaluation and treatment.
Physica A: Statistical Mechanics and its Applications 01/2010; · 1.68 Impact Factor
[show abstract][hide abstract] ABSTRACT: The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data, one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with fewer changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: (1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation and (2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.
[show abstract][hide abstract] ABSTRACT: The slime mold Dictyostelium discoideum is one of the model systems of biological pattern formation. One of the most successful answers to the challenge of establishing a spiral wave pattern in a colony of homogeneously distributed D. discoideum cells has been the suggestion of a developmental path the cells follow (Lauzeral and coworkers). This is a well-defined change in properties each cell undergoes on a longer time scale than the typical dynamics of the cell. Here we show that this concept leads to an inhomogeneous and systematic spatial distribution of spiral waves, which can be predicted from the distribution of cells on the developmental path. We propose specific experiments for checking whether such systematics are also found in data and thus, indirectly, provide evidence of a developmental path.
[show abstract][hide abstract] ABSTRACT: To understand the mechanism of action of the chaperone protein tapasin, which mediates loading of high-affinity peptides onto major histocompatibility complex (MHC) class I molecules in the antiviral immune response, we have performed numerical simulations of the class I-peptide binding process with four different mechanistic hypotheses from the literature, and tested our predictions by laboratory experiments. We find - in agreement of experimental and theoretical studies - that class I-peptide binding in cells is generally under kinetic control, and that tapasin introduces partial thermodynamic control to the process by competing with peptide for binding to class I. Based on our results, we suggest further experimental directions.
[show abstract][hide abstract] ABSTRACT: Recently, Kearns et al. [Kearns, M., Suri, S. and Montfort, N., An experimental study of the coloring problem on human subject networks, Science 313 (2006) 824–827] studied the topology dependence of graph coloring dynamics. In their empirical study, the authors analyze, how a network of human subjects acting as autonomous agents performs in solving a conflict-avoidance task (the graph coloring problem) for different network architectures. A surprising result was that the run-time of the empirical dynamics decreases with the number of shortcuts in a Watts–Strogatz small-world graph. In a simulation of the dynamics based on randomly selecting color conflicts for update, they observe a strong increase of the run-time with the number of shortcuts. Here, we propose classes of strategies, which are capable of explaining the decrease in run-time with an increasing number of shortcuts. We show that the agent's strategy, the graph topology, and the complexity of the problem (essentially given by the graph's chromatic number) interact nontrivially yielding unexpected insights into the problem-solving capacity of organizational structures.