Plant Computational Biology

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 7691

Special Issue Editors

1. NIPGR Aruna Asaf Ali Marg, New Delhi 110067 India
2. Bioinformatics Training Developer, Bioinformatics Training Facility, University of Cambridge, Downing Site, Cambridge CB2 3EA, UK
Interests: complex networks; computational genomics; food security; conservation; machine learning; phytochemistry
Consiglio Nazionale delle Ricerche, Istituto per i Sistemi Agricoli e Forestali del Mediterraneo (CNR-ISAFOM) U.O.S. Catania, Via Empedocle, 58, 95128 Catania, Italy
Interests: seed germination physiology; transcriptomics; gene expression; bioinformatics

Special Issue Information

Dear Colleagues,

Computational methods revolutionized the life sciences a generation ago, but a large number of botanists and ecologists are yet to appreciate this shift, resulting in a gap between cultural and conceptual changes. Computational biologists are now leading cutting-edge programs reflected in the large volume of data being generated and processed in the course of modern plant biology research, both in the lab and in the field. Moreover, the application of new bioinformatic tools (such as co-expression networks) to non-model organisms has paved the way to the identification of transcriptional regulatory elements specifically acting on relevant biosynthetic pathways or on specific physiological response to a certain environmental cue, at an unprecedented pace. This special Issue aims to highlight such work and thereby bridge the gap between basic plant biology and software development or its novel use in non-model organisms, so that Plant scientists may increase the probability of groundbreaking discoveries. We believe this issue will address ideas and new leads, involving the use of tools adapted from computer science, mathematics, statistics, chemistry, and other quantitative disciplines, towards developing analytical methods, algorithms, and/or models for interpreting biological information.

This Special Issue welcomes the submission of articles highlighting the application of computational techniques new or with a novel application to problems in plant biology, seed biology, genomics, biodiversity and climate change. The goal is to achieve a realistic overview of this exciting and interdisciplinary field that could be of great use to botanists.

Dr. Gitanjali Yadav
Dr. Giuseppe Puglia
Dr. Anyela Camargo Rodríguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational Biology;

    Biological Networks;

    Regulatory Networks;

    Transcriptomics;

    Big Data;

    Non-model organisms;

    Machine Learning;

Published Papers (2 papers)

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Research

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23 pages, 3234 KiB  
Article
Isolation and Comprehensive in Silico Characterisation of a New 3-Hydroxy-3-Methylglutaryl-Coenzyme A Reductase 4 (HMGR4) Gene Promoter from Salvia miltiorrhiza: Comparative Analyses of Plant HMGR Promoters
by Małgorzata Majewska, Łukasz Kuźma and Piotr Szymczyk
Plants 2022, 11(14), 1861; https://doi.org/10.3390/plants11141861 - 16 Jul 2022
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Abstract
Salvia miltiorrhiza synthesises tanshinones with multidirectional therapeutic effects. These compounds have a complex biosynthetic pathway, whose first rate limiting enzyme is 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR). In the present study, a new 1646 bp fragment of the S. miltiorrhiza HMGR4 gene consisting of a [...] Read more.
Salvia miltiorrhiza synthesises tanshinones with multidirectional therapeutic effects. These compounds have a complex biosynthetic pathway, whose first rate limiting enzyme is 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR). In the present study, a new 1646 bp fragment of the S. miltiorrhiza HMGR4 gene consisting of a promoter, 5′ untranslated region and part of a coding sequence was isolated and characterised in silico using bioinformatics tools. The results indicate the presence of a TATA box, tandem repeat and pyrimidine-rich sequence, and the absence of CpG islands. The sequence was rich in motifs recognised by specific transcription factors sensitive mainly to light, salicylic acid, bacterial infection and auxins; it also demonstrated many binding sites for microRNAs. Moreover, our results suggest that HMGR4 expression is possibly regulated during flowering, embryogenesis, organogenesis and the circadian rhythm. The obtained data were verified by comparison with microarray co-expression results obtained for Arabidopsis thaliana. Alignment of the isolated HMGR4 sequence with other plant HMGRs indicated the presence of many common binding sites for transcription factors, including conserved ones. Our findings provide valuable information for understanding the mechanisms that direct transcription of the S. miltiorrhiza HMGR4 gene. Full article
(This article belongs to the Special Issue Plant Computational Biology)
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Review

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22 pages, 2148 KiB  
Review
Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management
by Amanda Kim Rico-Chávez, Jesus Alejandro Franco, Arturo Alfonso Fernandez-Jaramillo, Luis Miguel Contreras-Medina, Ramón Gerardo Guevara-González and Quetzalcoatl Hernandez-Escobedo
Plants 2022, 11(7), 970; https://doi.org/10.3390/plants11070970 - 02 Apr 2022
Cited by 22 | Viewed by 4647
Abstract
Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites [...] Read more.
Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols. Full article
(This article belongs to the Special Issue Plant Computational Biology)
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