Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Beber, Moritz Emanuel; Fretter, Christoph; Jain, Shubham; Sonnenschein, Nikolaus; Müller‐Hannemann, Matthias; Hütt, Marc‐Thorsten

doi:10.1098/rsif.2012.0490

Cited by 40 publications

(38 citation statements)

References 69 publications

(124 reference statements)

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“…In particular, it is difficult to decide which low-level topological properties of the network data the null model networks should capture while at the same time the connectivity between vertices varies stochastically. Using an inappropriate null model in the statistical test might introduce a bias in the assignment of significance to subnetworks [30], [31]. The null model widely employed by original network motif detection preserves the in-degree and out-degree sequence.…”

Section: Methodsmentioning

confidence: 99%

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Extracting Labeled Topological Patterns from Samples of Networks

et al. 2013

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An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.

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Section: Methodsmentioning

confidence: 99%

“…In particular, rejected switches, which correspond to the transition from a network to itself, are also counted. Note that the edge-switching algorithm does not preserve the number of bidirectional links, which might be reasonable in several contexts [31].…”

Section: Methodsmentioning

confidence: 99%

Extracting Labeled Topological Patterns from Samples of Networks

et al. 2013

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“…Some critics have questioned Alon's methods of evaluating whether the frequency of subgraphs is beyond what would be expected by chance (cf. Beber, Fretter, Jain, Sonnenschein, Muller-Hannemann, and Hutt, 2012). Searching for overabundant circuit patterns in networks can, however, be useful as a heuristic regardless of the soundness of the null hypothesis used in the comparison, provided that this the network analysis is combined with other strategies that account for the details that network analyses neglect or distort.…”

Section: Figure 1 Example Of a Motif A Coherent Feedforward Loop Ofmentioning

confidence: 99%

Network analyses in systems biology: new strategies for dealing with biological complexity

et al. 2017

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Abstract:The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.

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“…The results of a wide range of our own investigations over the last years have their foundations in statistical physics and information theory [2,4,[25][26][27][28][29][30][31]. They have cemented the notion of a tight interplay of the regulatory network implemented via TFs and their binding sites (digital control); and the regulation implemented via alterations of chromosomal configuration and DNA compaction (analog control) in bacterial gene regulation (depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

Interplay of digital and analog control in time-resolved gene expression profiles

Beber

Sobetzko

Muskhelishvili

et al. 2016

EPJ Nonlinear Biomed Phys

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Background: Measuring the agreement between a gene expression profile and a known transcriptional regulatory network is an important step in the functional interpretation of bacterial physiological state. In this way, general design principles can be explored. One such interpretive framework is the relationship of digital control, that is, the impact of sequence-specific interactions, and analog control, i.e., the extent of the influence of chromosomal structure. Methods and Results:Here, we present time-resolved gene expression profiles of Escherichia coli's growth cycle as measured by RNA-seq. We extend methods which have been developed for discrete sets of differentially expressed genes and apply them to the wild type and two mutant time-series for which the global transcriptional regulators fis and hns were inactivated. We test our continuous methods using simulated 'expression profiles' generated from random Boolean network dynamics where we observe a clear trade-off between maximum response and level of detail included. In the real time-course expression data, we find strong interdependent changes of digital and analog control during the exponential growth phase and a dominance of analog control during the stationary phase. Conclusions: Our investigation puts forward a simple and reliable method for quantifying the match between time-resolved gene expression profiles and a transcriptional regulatory network. The method reveals a systematic compensatory interplay of digital and analog control in the genetic regulation of E. coli's growth cycle.

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Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Cited by 40 publications

References 69 publications

Extracting Labeled Topological Patterns from Samples of Networks

Extracting Labeled Topological Patterns from Samples of Networks

Network analyses in systems biology: new strategies for dealing with biological complexity

Interplay of digital and analog control in time-resolved gene expression profiles

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