A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks

Narendra, Varun; Lytkin, Nikita I.; Aliferis, Constantin F.; Statnikov, Alexander

doi:10.1016/j.ygeno.2010.10.003

Cited by 40 publications

(39 citation statements)

References 38 publications

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“…For example, we followed this approach to characterize the metabolic adaptations of Mycobacterium tuberculosis to hypoxia and identify metabolic alterations required for adaptation to a lifestyle of low metabolic activity [10]. Alternatively, computational approaches have been developed to infer regulatory networks from gene expression data [11], which in turn have been integrated with metabolic network models to describe the adaptation of an organism to different conditions [12-15]. …”

Section: Introductionmentioning

confidence: 99%

Bridging the gap between gene expression and metabolic phenotype via kinetic models

2013

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BackgroundDespite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.ResultsWe developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.ConclusionsOur simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

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Section: Introductionmentioning

confidence: 99%

Bridging the gap between gene expression and metabolic phenotype via kinetic models

2013

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“…However, as argued by Narendra et al (2011), this series has not provided a definitive answer as to what the best performing techniques are for genome-scale observational data. In addition, the assessments are mostly based on synthetic datasets.…”

Section: Resultsmentioning

confidence: 97%

“…Assessments of methods are performed using metrics, usually with area under receiver operating characteristics (AUROC), F-score, or area under precision-recall curve (AUPR) (Altay and Emmert-Streib, 2010b;Narendra et al, 2011). Nonetheless, in our approach, since the interaction databases of the literature are far from being complete, these may cause too many FPs and thus are not suitable metrics.…”

Section: Resultsmentioning

confidence: 99%

“…The only real gene expression data used for the assessment of the methods was in DREAM2 (Stolovitzky et al, 2009). It has been argued that as this competition involved a single dataset to which many methods had been applied, the results may be over-fitted and thus may not generalize to other datasets (Narendra et al, 2011). Therefore, our proposed framework and the software ganet are a significant contribution to global assessment of networking methods on a single platform that is independent from the dataset.…”

Section: Resultsmentioning

confidence: 99%

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Global assessment of network inference algorithms based on available literature of gene/protein interactions

Altay¹,

N²,

Neal³

2013

Turk J Biol

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We propose a framework that uses the available gene/protein interaction databases of the literature as a universal benchmark in order to globally assess the inference performances of gene network inference algorithms. We also developed an R software package for convenient use of the framework, which can also be used in general as a quick tool to search in the literature for available validations of interactions. We applied the proposed approach to 2 publicly available prostate cancer gene expression datasets and a large breast cancer gene expression dataset. The results revealed different aspects and superiority of algorithms that had not been compared previously in the available literature. Our approach allowed the assessing and comparing of the algorithms on a real dataset of a size of around 30,000 probes, which showed the strengths and weaknesses of the algorithms from different points of view rather than conventional approaches. We further show that our approach provides a unique advantage in assessing the performance of an inference method when applied to a new dataset and thus sheds light on the results of a de novo application, which would be obscure without our approach.

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“…E. coli [17]–[19], [21]. Interestingly, inference of E. Coli networks seems to be an easier problem than inference of S. cerevisiae networks.…”

Section: Discussionmentioning

confidence: 99%

De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae

Kemmeren

Gresham

et al. 2014

PLoS ONE

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De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

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A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks

Cited by 40 publications

References 38 publications

Bridging the gap between gene expression and metabolic phenotype via kinetic models

Bridging the gap between gene expression and metabolic phenotype via kinetic models

Global assessment of network inference algorithms based on available literature of gene/protein interactions

De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae

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