Consistency analysis of metabolic correlation networks

Müller-Linow, Mark; Weckwerth, Wolfram; Hütt, Marc‐Thorsten

doi:10.1186/1752-0509-1-44

Cited by 48 publications

(47 citation statements)

References 40 publications

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“…However, initial metabolome experiments (Roessner et al, 2001) as well as simulation studies (Steuer et al, 2003;Müller-Linow et al, 2007) have shown that correlations do not necessarily reflect the pathway architecture. More specifically, profiling studies of mainly primary metabolites (Roessner et al, 2001) have shown that most metabolite pairs have low correlation coefficients and only a few metabolite pairs are highly correlated.…”

Section: Cspp Network Do Not Show a Small World Behaviormentioning

confidence: 99%

Systematic Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks

et al. 2014

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Plant metabolomics is increasingly used for pathway discovery and to elucidate gene function. However, the main bottleneck is the identification of the detected compounds. This is more pronounced for secondary metabolites as many of their pathways are still underexplored. Here, an algorithm is presented in which liquid chromatography-mass spectrometry profiles are searched for pairs of peaks that have mass and retention time differences corresponding with those of substrates and products from well-known enzymatic reactions. Concatenating the latter peak pairs, called candidate substrate-product pairs (CSPP), into a network displays tentative (bio)synthetic routes. Starting from known peaks, propagating the network along these routes allows the characterization of adjacent peaks leading to their structure prediction. As a proof-of-principle, this high-throughput cheminformatics procedure was applied to the Arabidopsis thaliana leaf metabolome where it allowed the characterization of the structures of 60% of the profiled compounds. Moreover, based on searches in the Chemical Abstract Service database, the algorithm led to the characterization of 61 compounds that had never been described in plants before. The CSPP-based annotation was confirmed by independent MS n experiments. In addition to being high throughput, this method allows the annotation of low-abundance compounds that are otherwise not amenable to isolation and purification. This method will greatly advance the value of metabolomics in systems biology.

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Section: Cspp Network Do Not Show a Small World Behaviormentioning

confidence: 99%

Systematic Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks

et al. 2014

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“…For instance, in a recent study by Muller-Linow et al (2007), GC-MS analysis could only map 39 of the measured metabolites on the metabolic pathways. Another important issue concerning the coverage of metabolic pathways is the potential bias introduced by the extraction procedure and the equipment set-up.…”

Section: Coverage Of Metabolic Pathways By Ms Datamentioning

confidence: 99%

Data Integration, Metabolic Networks and Systems Biology

Redestig

Szymański

Hirai

et al. 2011

Annual Plant Reviews Volume 43

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As analytical techniques and data pre-processing methods continue to improve, the bottleneck of metabolomics is shifting towards later stages of data analysis and biological interpretation. High-coverage metabolomics is only possible when combining data from multiple platforms necessitating efficient methods for data integration. Metabolomic data sets with high coverage provide a unique opportunity to estimate and study metabolic networks. Once established, these networks can provide a backbone for systems biology approaches where the aim is to construct fundamental models of metabolic regulation. In this chapter, we provide an overview of status of these topics and describe current methods and tools, their drawbacks and advantages for integrative plant metabolomics.

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“…In systems biology, the comparison of metabolomics data with metabolic networks belongs to the same class of statistical problems (see, for example, [11,12]). A related question is network inference, i.e., how to estimate interaction networks from dynamical data.…”

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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Consistency analysis of metabolic correlation networks

Cited by 48 publications

References 40 publications

Systematic Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks

Systematic Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks

Data Integration, Metabolic Networks and Systems Biology

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

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