Integration of Plant Metabolomics Data with Metabolic Networks: Progresses and Challenges

Current analytical technologies and bioinformatic resources for plant metabolomics data

Plant Biotechnology Reports ◽

10.1007/s11816-021-00703-3 ◽

2021 ◽

Author(s):

Chigateri M. Vinay ◽

Sanjay Kannath Udayamanoharan ◽

Navya Prabhu Basrur ◽

Bobby Paul ◽

Padmalatha S. Rai

Keyword(s):

Inorganic Compounds ◽

Biological Data ◽

Biological Information ◽

Metabolomics Data ◽

Primary And Secondary Metabolites ◽

Chemical Structures ◽

Plant Metabolomics ◽

Wide Range ◽

Analytical Tools

AbstractPlant metabolome as the downstream product in the biological information of flow starting from genomics is highly complex, and dynamically produces a wide range of primary and secondary metabolites, including ionic inorganic compounds, hydrophilic carbohydrates, amino acids, organic compounds, and compounds associated with hydrophobic lipids. The complex metabolites present in biological samples bring challenges to analytical tools for separating and characterization of the metabolites. Analytical tools such as nuclear magnetic resonance (NMR) and mass spectrometry have recently facilitated the separation, characterization, and quantification of diverse chemical structures. The massive amount of data generated from these analytical tools need to be handled using fast and accurate bioinformatics tools and databases. In this review, we focused on plant metabolomics data acquisition using various analytical tools and freely available workflows from raw data to meaningful biological data to help biologists and chemists to move at the same pace as computational biologists.

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A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

BMC Bioinformatics ◽

10.1186/1471-2105-7-540 ◽

2006 ◽

Vol 7 (1) ◽

Cited By ~ 31

Author(s):

I Emrah Nikerel ◽

Wouter A van Winden ◽

Walter M van Gulik ◽

Joseph J Heijnen

Keyword(s):

Steady State ◽

Metabolic Networks ◽

Metabolomics Data

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Transcriptomics to Metabolomics

Advances in Bioinformatics and Biomedical Engineering - Applying Big Data Analytics in Bioinformatics and Medicine ◽

10.4018/978-1-5225-2607-0.ch008 ◽

2018 ◽

pp. 188-206 ◽

Cited By ~ 5

Author(s):

Ankush Bansal ◽

Pulkit Anupam Srivastava

Keyword(s):

Biological Networks ◽

Metabolic Networks ◽

Topological Analysis ◽

In Silico Analysis ◽

Small World ◽

Recent Decade ◽

Bipartite Networks ◽

Metabolomics Data ◽

Scale Free ◽

Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

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Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

BioMed Research International ◽

10.1155/2016/8313272 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 26

Author(s):

Albert Batushansky ◽

David Toubiana ◽

Aaron Fait

Keyword(s):

Metabolic Networks ◽

Cell Metabolism ◽

Data Sets ◽

Metabolomics Data ◽

Network Generation ◽

Carcinoma Cell Lines ◽

Biological Studies ◽

Metabolic Interactions ◽

Cancer Cell Metabolism ◽

Breast Carcinoma Cell Lines

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

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Transcriptomics to Metabolomics

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch014 ◽

2019 ◽

pp. 361-379

Author(s):

Ankush Bansal ◽

Pulkit Anupam Srivastava

Keyword(s):

Biological Networks ◽

Metabolic Networks ◽

Topological Analysis ◽

In Silico Analysis ◽

Small World ◽

Recent Decade ◽

Bipartite Networks ◽

Metabolomics Data ◽

Scale Free ◽

Gene Regulatory

A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

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The photographer and the greenhouse: how to analyse plant metabolomics data

Phytochemical Analysis ◽

10.1002/pca.1181 ◽

2009 ◽

Vol 21 (1) ◽

pp. 48-60 ◽

Cited By ~ 25

Author(s):

Jeroen J. Jansen ◽

Suzanne Smit ◽

Huub C.J. Hoefsloot ◽

Age K. Smilde

Keyword(s):

Metabolomics Data ◽

Plant Metabolomics

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Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolites ◽

10.3390/metabo10080303 ◽

2020 ◽

Vol 10 (8) ◽

pp. 303 ◽

Cited By ~ 2

Author(s):

Svetlana Volkova ◽

Marta R. A. Matos ◽

Matthias Mattanovich ◽

Igor Marín de Mas

Keyword(s):

Data Integration ◽

Metabolic Networks ◽

Kinetic Modelling ◽

Cell Metabolism ◽

Cell Phenotype ◽

Metabolomics Data ◽

Metabolic Modelling ◽

Metabolomic Data ◽

Close Relationship ◽

Underlying Mechanisms

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

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Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology

Briefings in Bioinformatics ◽

10.1093/bib/bbz104 ◽

2019 ◽

Cited By ~ 6

Author(s):

Peter D Karp ◽

Peter E Midford ◽

Richard Billington ◽

Anamika Kothari ◽

Markus Krummenacker ◽

...

Keyword(s):

Data Analysis ◽

Metabolic Networks ◽

Metabolic Flux ◽

Metabolic Model ◽

Supplementary Information ◽

Biological Knowledge ◽

Omics Data ◽

Dynamic Interactions ◽

Metabolomics Data ◽

Knowledge Resources

Abstract Motivation Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets. Results In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages. Availability The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com. Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

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