Integrative omics of plants in response to stress conditions play more crucial roles in the post-genomic era. High-quality genomic data provide more deeper understanding of how plants to survive under environmental stresses. This book is focused on concluding the recent progress in the Protein and Proteome Atlas in plants under different stresses. It covers various aspects of plant protein ranging from agricultural proteomics, structure and function of proteins, and approaches for protein identification and quantification. A total of 27 papers including two timely reviews have contributed to this Special Issue. In the first part with the topic of “Comparative Proteomics of Different Plants”, six papers were included to describe the phenotypic changes and proteomic analyses of different plants under different conditions. Then, another six papers with the topic of “Proteomics of Plants under Osmotic Stress” were included to describe the recent comparative proteomics analyses of plants under osmotic stress, particularly the drought and salinity stresses in leaves of certain plant species. The other proteomics studies on several energy plants and economic crops were reported to demonstrate the recent omics studies on different plants during their development processes. More stress responsive genes and proteins in these plants were identified. These target genes and proteins are important candidates for further functional validation in economic plants and crops.
Integrative omics of plants in response to stress conditions play more crucial roles in the post-genomic era. High-quality genomic data provide more deeper understanding of how plants to survive under environmental stresses. This book is focused on concluding the recent progress in the Protein and Proteome Atlas in plants under different stresses. It covers various aspects of plant protein ranging from agricultural proteomics, structure and function of proteins, and approaches for protein identification and quantification.
In the era of climate change, the resilience of crop plants is vital for global food security. Abiotic Stress in Crop Plants - Ecophysiological Responses and Molecular Approaches addresses the challenges posed by stressors like extreme temperatures, drought, salinity, and flooding. This comprehensive volume features 13 chapters that explore ecophysiology and plant responses to environmental stress, adaptation mechanisms, strategies plants use to survive under adverse conditions, and genetic and molecular bases of stress tolerance. By integrating these areas, the book offers a holistic view of plant responses to abiotic stress, compiling recent advancements and cutting-edge research. It is an essential resource for scientists, researchers, and students dedicated to enhancing crop resilience and promoting sustainable agriculture.
Plants are continuously exposed to a wide range of environmental conditions, including cold, drought, salt, heat, which have major impact on plant growth and development. To survive, plants have evolved complex physiological and biochemical adaptations to cope with a variety of adverse environmental stresses. Among them, reactive oxygen species (ROS) are key regulators and play pivotal roles during plant stress responses, which are thought to function as early signals during plant abiotic stress responses. ROS were long regarded as unwanted and toxic by-products of physiological metabolism. However, ROS are now recognized as central players in the complex signaling network of cells. Therefore, a fine-tuning control between ROS production and scavenging pathways is essential to maintain non-toxic levels in planta under stressful conditions through enzymatic and non-enzymatic antioxidant defense systems. We focus on the roles of ROS during plant abiotic stress responses in this Research Topic. Plant responses to multiple abiotic stresses and effects of hormones and chemicals on plant stress responses have been carefully studies. Although functions of several stress responsive genes have been characterized and possible interactions between hormones and ROS are discussed, future researches are needed to functionally characterize ROS regulatory and signaling transduction pathways.
Explore and advance bioinformatics and systems biology tools for crop breeding programs in this practical resource for researchers Plant biology and crop breeding have produced an immense amount of data in recent years, from genomics to interactome and beyond. Bioinformatics tools, which aim at analyzing the vast quantities of data produced by biological research and processes, have developed at a rapid pace to meet the challenges of this vast data trove. The resulting field of bioinformatics and systems biology is producing increasingly rich and transformative research. Bioinformatics for Plant Research and Crop Breeding offers an overview of this field, its recent advances, and its wider applications. Drawing on a range of analytical and data-science tools, its foundation on an in-silico platform acquired multi-omics makes it indispensable for scientists and researchers alike. It promises to become ever more relevant as new techniques for generating and organizing data continue to transform the field. Bioinformatics for Plant Research and Crop Breeding readers will also find: A focus on emerging trends in plant science, sustainable agriculture, and global food security Detailed discussion of topics including plant diversity, plant stresses, nanotechnology in agriculture, and many others Applications incorporating artificial intelligence, machine learning, deep learning and more Bioinformatics for Plant Research and Crop Breeding is ideal for researchers and scientists interested in the potential of OMICs, and bioinformatic tools to aid and develop crop improvement programs.
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis
This edited volume summarizes the recent advancements made in plant science including molecular biology and genome editing , particularly in the development of novel pathways tolerant to climate change-induced stresses such as drought, extreme temperatures, cold, salinity, flooding, etc. These stresses are liable for decrease in yields in many crop plants at global level. Till date conventional plant breeding approaches have resulted in significant improvement of crop plants for producing higher yields during adverse climatic conditions. However, the pace of improvement through conventional plant breeding needs to be accelerated in keeping with the growing demand of food and increasing human populationl, particularly in developing world. This book serves as a comprehensive reference material for researchers, teachers, and students involved in climate change-related abiotic stress tolerance studies in plants.
