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Plants
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Deciphering Plant Molecular Data Using Computational Methods
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Deciphering Plant Molecular Data Using Computational Methods

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 608

Special Issue Editor

Dr. Rajeev K. Azad

E-Mail Website
Guest Editor
1. Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
2. BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
3. Department of Mathematics, University of North Texas, Denton, TX 76203, USA
Interests: plants bioinformatics; computational genomics, genome evolution, pathogenomics, metagenomics; gene prediction, structural variation detection, disease gene identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-throughput sequencing technologies continue to generate vast amounts of plant molecular data, advancing the frontier of plant biology research and making significant contributions to the understanding of plants. Computational analysis has become an indispensable tool in unraveling complex plant molecular networks.

This Special Issue aims to provide a platform for researchers to share the latest findings and advancements in the use of computational methods for unraveling the mysteries of plant molecular networks. We welcome original research articles, critical review papers and perspectives that delve into various aspects of this field, including the genomics, transcriptomics, proteomics, metabolomics and epigenomics of plants. Additionally, we encourage contributions on data processing, analysis and visualization techniques that pave the way for novel insights into plant molecular mechanisms.

Dr. Rajeev K. Azad
Guest Editor

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

  • plant molecular networks
  • plant biology
  • computational data
  • genomics
  • transcriptomics
  • proteomics
  • metabolomics

Published Papers (1 paper)

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Research

15 pages, 4155 KiB  
Article
Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
by Sofia A. Bengoa Luoni, Riccardo Ricci, Melanie A. Corzo, Genc Hoxha, Farid Melgani and Paula Fernandez
Plants 2024, 13(14), 1998; https://doi.org/10.3390/plants13141998 - 22 Jul 2024
Viewed by 335
Abstract
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of [...] Read more.
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase. Full article
(This article belongs to the Special Issue Deciphering Plant Molecular Data Using Computational Methods)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

A guide to metabolic network modeling for plant biology

Xiaolan Rao and Wei Liu

Abstract

Plants produce a wide range of compounds that play diverse roles in plant growth and development, and in response to abiotic and biotic stresses. Understanding metabolic pathway fluxes in plants is essential in guiding strategies to direct metabolism for crop improvement, plant natural product industry, and human nutrition and health. In the past decade, metabolic network modeling has become a predominant tool for integration, quantitation, and prediction of the spatial and temporal distribution of metabolic flows. In this review, we provide a practical protocol for mathematical modeling of metabolic networks, including steady-state modeling (flux balance analysis (FBA) and metabolic flux analysis (MFA)) and dynamic modeling. The practical application of mathematical modeling will provide significant insights in the structure and regulation of plant metabolic networks.

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