Modeling regulatory networks using machine learning for systems metabolic engineering

Lee, Byung Tae; Lee, Sang Yup; Kim, Hyun Uk

doi:10.1016/j.copbio.2020.02.014

Cited by 19 publications

(11 citation statements)

References 55 publications

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“…Over the past decade, RNA sequencing (RNA-seq) has emerged as an efficient, high-throughput method to probe the expression state of a cell population. Advances in next-generation sequencing have accelerated the creation of large RNA-seq datasets (1)(2)(3)(4), which subsequently enabled the successful development and application of machine learning methods to advance our understanding of transcriptional regulation (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decade, RNA sequencing (RNA-seq) has emerged as an efficient, high-throughput method to determine the expression state of a cell population. Large RNA-seq datasets (ENCODE Project Consortium, 2012; GTEx Consortium, 2015; Leader et al, 2018; Sastry et al, 2019; Ziemann et al, 2019) have enabled the development and application of machine learning methods to advance our understanding of expression and transcriptional regulation (Avsec et al, 2021; Kelley et al, 2018; Kwon et al, 2020; Sastry et al, 2019; Zhang et al, 2019; Zrimec et al, 2020). As datasets continue to grow, analytic methods must keep pace to convert this data to biological knowledge.…”

Section: Introductionmentioning

confidence: 99%

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A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Sastry

et al. 2021

Preprint

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Uncovering the structure of the transcriptional regulatory network (TRN) that modulates gene expression in prokaryotes remains an important challenge. Transcriptomics data is plentiful, necessitating the development of scalable methods for converting this data into useful knowledge about the TRN. Previously, we published the PRECISE dataset for Escherichia coli K-12 MG1655, containing 278 RNA-seq datasets created using a standardized protocol. Here, we present PRECISE 2.0, which is nearly three times the size of the original PRECISE dataset and also created using a standardized protocol. We analyze PRECISE 2.0 at multiple scales, demonstrating multiple analytical strategies for extracting knowledge from this dataset. Specifically, we: (1) highlight patterns in gene expression across the dataset; (2) utilize independent component analysis to extract 218 independently modulated groups of genes (iModulons) that describe the TRN at the systems level; (3) demonstrate the utility of iModulons over traditional differential expression analysis; and (4) uncover 6 new potential regulons. Thus, PRECISE 2.0 is a large-scale, high-quality transcriptomics dataset which may be analyzed at multiple scales to yield important biological insights.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Sastry

et al. 2021

Preprint

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“…AI approaches provide the platform to analyze huge, various, and useless datasets such as the generation of genome sequencing/photo imaging over conventional analytical strategies [ 15 , 62 ]. Recently, the AI approach has been explicitly employed in varied research fields of phenomics and genomics, such as: analysing genome assembly and genome-specific algorithms [ 26 ]; broad-range data analysis to mitigate multiplex biological complications in metabolomics, proteomics, genomics, transcriptomics, as well as systematic biology [ 62 , 63 ]; interpretation of gene expression cascades [ 64 , 65 ]; identification of significant SNPs in polyploid plants [ 66 ]; high-throughput crop stress phenotyping [ 41 , 67 ].…”

Section: Linking Of Crop Genome To Phenome With Aimentioning

confidence: 99%

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Khan

Wang

et al. 2022

IJMS

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Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

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“…An exciting avenue for synthetic biology and metabolic engineering is the implementation of machine learning algorithms to help understand the metabolic and regulatory networks of host organisms to optimise existing pathways and develop new synthetic routes [ 114 , 115 ].…”

Section: Machine Learningmentioning

confidence: 99%

Synthetic Biology towards Improved Flavonoid Pharmacokinetics

et al. 2021

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Flavonoids are a structurally diverse class of natural products that have been found to have a range of beneficial activities in humans. However, the clinical utilisation of these molecules has been limited due to their low solubility, chemical stability, bioavailability and extensive intestinal metabolism in vivo. Recently, the view has been formed that site-specific modification of flavonoids by methylation and/or glycosylation, processes that occur in plants endogenously, can be used to improve and adapt their biophysical and pharmacokinetic properties. The traditional source of flavonoids and their modified forms is from plants and is limited due to the low amounts present in biomass, intrinsic to the nature of secondary metabolite biosynthesis. Access to greater amounts of flavonoids, and understanding of the impact of modifications, requires a rethink in terms of production, more specifically towards the adoption of plant biosynthetic pathways into ex planta synthesis approaches. Advances in synthetic biology and metabolic engineering, aided by protein engineering and machine learning methods, offer attractive and exciting avenues for ex planta flavonoid synthesis. This review seeks to explore the applications of synthetic biology towards the ex planta biosynthesis of flavonoids, and how the natural plant methylation and glycosylation pathways can be harnessed to produce modified flavonoids with more favourable biophysical and pharmacokinetic properties for clinical use. It is envisaged that the development of viable alternative production systems for the synthesis of flavonoids and their methylated and glycosylated forms will help facilitate their greater clinical application.

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Modeling regulatory networks using machine learning for systems metabolic engineering

Cited by 19 publications

References 55 publications

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Synthetic Biology towards Improved Flavonoid Pharmacokinetics

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