Recent Computational Approaches to Understand Gene Regulation: Mining Gene Regulation In Silico

Abnizova, Irina; Subhankulova, T.; Gilks, Walter R.

doi:10.2174/138920207780368150

Cited by 5 publications

(3 citation statements)

References 203 publications

(262 reference statements)

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“…In some instances, there exists a correlation between binding energy and substitution rate (Brown and Callan 2004), but this may not always be the case (Kotelnikova et al 2005). Furthermore, nonbinding nucleotides may exert some effect on transcription (Mai et al 2000;Mirny and Gelfand 2002;Abnizova et al 2007;Wozniak and Hughes 2008) and potentially on fitness.…”

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confidence: 99%

Evidence That Purifying Selection Acts on Promoter Sequences

Arthur

Ruvinsky

2011

Genetics

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confidence: 99%

Evidence That Purifying Selection Acts on Promoter Sequences

Arthur

Ruvinsky

2011

Genetics

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“…In contrast, the comparatively more mature fields of genomics and transcriptomics have yielded a variety of experimental approaches to explore transcriptional regulation, providing sufficient data to motivate the development of computational approaches to map out gene regulatory networks [ 10 , 11 ]. Some of the latest computational approaches have leveraged spatial and temporal gene expression data, environmental data, and rapid gene perturbation data to better predict and understand gene regulation [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

et al. 2021

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Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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“…In contrast, the comparatively more mature fields of genomics and transcriptomics have yielded a variety of experimental approaches to explore transcriptional regulation, providing sufficient data to motivate the development of computational approaches to map out gene regulatory networks [10,11]. Some of the latest computational approaches have leveraged spatial and temporal gene expression data, environmental data, and rapid gene perturbation data to better predict and understand gene regulation [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

SCOUR: A stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

Lee

Nguyen

Orosco

et al. 2021

Preprint

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Background: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms -- two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: Stepwise Classification Of Unknown Regulation, or SCOUR. Results: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32-88% for noiseless data, 9.2-49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

show abstract

Recent Computational Approaches to Understand Gene Regulation: Mining Gene Regulation In Silico

Cited by 5 publications

References 203 publications

Evidence That Purifying Selection Acts on Promoter Sequences

Evidence That Purifying Selection Acts on Promoter Sequences

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

SCOUR: A stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

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