During the last decades, the advent and generalization of high-throughput methodologies have allowed scientists to address biological questions system-wide. Consequently, comprehensive characterizations of plant responses can be conducted, including information from transcriptome, proteome, and metabolome profiles. In parallel, informatic resources have evolved to meet scientists' demands to analyze and integrate data. Under this context, Systems Biology has emerged as the discipline that integrates aspects within large-scale data generation and analysis. In the field of plant science, systemic studies are being focused on to gain insight into how plants behave under given conditions or situations. For example, in developmental events, stress responses, integration of environmental cues, or analysis of consequences of mutations. More recently, the large volume of available datasets to the community has allowed the implementation of artificial intelligence-based models that train models to anticipate how plants would respond according to the existing knowledge.
Despite the implementation of system-wide technologies to investigate plant biological processes, the enormous potential to provide systemic interpretations has not been completely explored in most cases. In this regard, Systems Biology includes versatile strategies to integrate complex datasets ranging from co-expression and or co-regulation networks to predictive models that can contribute to generating knowledge from holistic perspectives and anticipate trajectories for molecular responses. Importantly, given that such approaches are based on the fact that biological systems consist of the interplay of interconnected events, the impact of local reconfigurations on the entire plant homeostasis network could be elucidated. This point is critical when it comes to translating biotechnological strategies from model plants to crops to avoid potential trade-offs.
Consequently, the adoption of systemic analysis of high-throughput data and modeling is necessary for the proper investigation of plant biological responses to frequent environmental cues or challenges, taking into consideration the whole organism.
This Research Topic aims to provide plant biologists with a series of exemplary and high-quality studies that address relevant questions in the field of plant biology; using multi-disciplinary and state-of-the-art methods for high-throughput profiling and systemic analysis to provide general interpretation in molecular responses.
We welcome contributions that focus on, but are not limited to, the following:
- Comparative analysis of responses among species
- Explanation of evolutionary forces through co-expression or co-regulated events
- Demonstration of singular rewiring in combinatorial stress responses compared to individual stressors
- Elucidation of large-scale protein-protein interaction maps
- The elaboration of machine-learning models or predictive algorithms/machine-learning applications
During the last decades, the advent and generalization of high-throughput methodologies have allowed scientists to address biological questions system-wide. Consequently, comprehensive characterizations of plant responses can be conducted, including information from transcriptome, proteome, and metabolome profiles. In parallel, informatic resources have evolved to meet scientists' demands to analyze and integrate data. Under this context, Systems Biology has emerged as the discipline that integrates aspects within large-scale data generation and analysis. In the field of plant science, systemic studies are being focused on to gain insight into how plants behave under given conditions or situations. For example, in developmental events, stress responses, integration of environmental cues, or analysis of consequences of mutations. More recently, the large volume of available datasets to the community has allowed the implementation of artificial intelligence-based models that train models to anticipate how plants would respond according to the existing knowledge.
Despite the implementation of system-wide technologies to investigate plant biological processes, the enormous potential to provide systemic interpretations has not been completely explored in most cases. In this regard, Systems Biology includes versatile strategies to integrate complex datasets ranging from co-expression and or co-regulation networks to predictive models that can contribute to generating knowledge from holistic perspectives and anticipate trajectories for molecular responses. Importantly, given that such approaches are based on the fact that biological systems consist of the interplay of interconnected events, the impact of local reconfigurations on the entire plant homeostasis network could be elucidated. This point is critical when it comes to translating biotechnological strategies from model plants to crops to avoid potential trade-offs.
Consequently, the adoption of systemic analysis of high-throughput data and modeling is necessary for the proper investigation of plant biological responses to frequent environmental cues or challenges, taking into consideration the whole organism.
This Research Topic aims to provide plant biologists with a series of exemplary and high-quality studies that address relevant questions in the field of plant biology; using multi-disciplinary and state-of-the-art methods for high-throughput profiling and systemic analysis to provide general interpretation in molecular responses.
We welcome contributions that focus on, but are not limited to, the following:
- Comparative analysis of responses among species
- Explanation of evolutionary forces through co-expression or co-regulated events
- Demonstration of singular rewiring in combinatorial stress responses compared to individual stressors
- Elucidation of large-scale protein-protein interaction maps
- The elaboration of machine-learning models or predictive algorithms/machine-learning applications
