With the advancements in computer technology, applied graph theory has become a popular and affordable tool in the natural sciences for the analysis of complex datasets. With the plethora of vast and complex datasets in modern biology, network analysis is often used to analyze the systemic interplay of biological components, aiding in the identification of target genes or essential protein components. Similarly, in the plant sciences network analysis is increasingly applied to elucidate the complex cross-talk of different molecular components, aiding in the identification of key elements and coherent structures, as well as in the understanding of the molecular dynamics of the plant. Nevertheless, graph theory, and its various tools, is still not exploited to its full potential by biologists. We believe that accessibility to these approaches should increase and not be restricted to computational biologists. However, when implementing mathematical tools, care should be put in fitting the best method to our data and in interpreting the results. The usage of network-based methods should be broad and easier, not only to the few but to all biologists within the scientific community. This Frontiers Research Topic will feature current approaches and challenges on molecular networks in plants. Topics to be covered include, but are not limited to, genome-scale networks, gene regulatory networks, correlation analysis-based networks, metabolite pathway networks, molecular component interaction networks, and novel strategies and algorithms for the analysis of molecular networks in plants.
With the advancements in computer technology, applied graph theory has become a popular and affordable tool in the natural sciences for the analysis of complex datasets. With the plethora of vast and complex datasets in modern biology, network analysis is often used to analyze the systemic interplay of biological components, aiding in the identification of target genes or essential protein components. Similarly, in the plant sciences network analysis is increasingly applied to elucidate the complex cross-talk of different molecular components, aiding in the identification of key elements and coherent structures, as well as in the understanding of the molecular dynamics of the plant. Nevertheless, graph theory, and its various tools, is still not exploited to its full potential by biologists. We believe that accessibility to these approaches should increase and not be restricted to computational biologists. However, when implementing mathematical tools, care should be put in fitting the best method to our data and in interpreting the results. The usage of network-based methods should be broad and easier, not only to the few but to all biologists within the scientific community. This Frontiers Research Topic will feature current approaches and challenges on molecular networks in plants. Topics to be covered include, but are not limited to, genome-scale networks, gene regulatory networks, correlation analysis-based networks, metabolite pathway networks, molecular component interaction networks, and novel strategies and algorithms for the analysis of molecular networks in plants.
