About this Research Topic
The elucidation of the molecular mechanisms underlying embryonic development, cellular differentiation, the evolutionary basis of development, conserved molecular pathways and genetic traits, and phylogenetic relationships are all topics of significant relevance for biological sciences and medicine. As our molecular and phenotypical knowledge of the developmental similarities between distinct organisms grows, we can effectively create new therapeutic approaches to numerous birth-related diseases, prevent genetic disorders, and further improve the understanding of the evolutionary process. However, the developmental process is not only composed of a myriad of bioprocesses fine-tuned in a spatio-temporal manner, but also influenced by feeding habits, lifestyle choices, exposure to chemicals, pollution, and several other external agents. This web of distinct factors creates a challenging scenario. Piecing together this vast collection of possible relationships, obtained from different types of biological and chemical data, requires integrative approaches and new computational tools that allow the visualization and analysis of such complex associations.
In this Research Topic, we are interested in studies that address the elucidation of molecular mechanisms underlying the embryonic development, tissue formation, or cellular differentiation, as well as for reports of new genes, RNAs, or proteins linked to the same topics. Studies must contain integrative approaches, such as systems biology, which apply new, or comprehensive protocols, to further explore their data. Works focused on evolutionary developmental biology are highly supported;
• Phylogenetic studies describing new evolutionary relationships on genes that impact on development. Possible genotype-phenotype associations are encouraged;
• New computational tools that could aid in analyzing phylogenetic data, large-scale biological data (e.g., microarray, RNA-seq, ChIP-seq, etc.), biological networks, and gene regulatory networks. Studies must be focused on answering biological questions related to embryonic development, tissue formation, or cellular differentiation. Machine learning algorithms trained and tested with up-to-date datasets are encouraged as long as they focus on answering a biological question of interest of this Research Topic, or overcoming some known problem in dealing with this kind of data.
• The description of new gene regulatory networks related to development, tissue formation, or differentiation. The manuscript must bring new algorithms and/or sound integrative approaches;
• Toxicological effects of different substances and compounds on development. Same as other topics, studies must apply integrative approaches to explore their data further;
• New protein structures, and molecular and quantum dynamics simulations are welcome as long as they report and/or analyze proteins related to this research topic's subjects of interest. A combination of new structures and simulations are highly favored;
• Combinatorial analysis of different types or a vast amount of expression data types is considered, as long as they are combined with integrative approaches.
Please note that we will not consider works describing new SNPs or SNVs, nor will we accept works that describe new phenotypes but do not address their molecular mechanisms. Descriptions of new algorithms that do not attempt to address a biological question will not be considered either.
In this Research Topic, we are interested in studies that address the elucidation of molecular mechanisms underlying the embryonic development, tissue formation, or cellular differentiation, as well as for reports of new genes, RNAs, or proteins linked to the same topics. Studies must contain integrative approaches, such as systems biology, which apply new, or comprehensive protocols, to further explore their data. Works focused on evolutionary developmental biology are highly supported;
• Phylogenetic studies describing new evolutionary relationships on genes that impact on development. Possible genotype-phenotype associations are encouraged;
• New computational tools that could aid in analyzing phylogenetic data, large-scale biological data (e.g., microarray, RNA-seq, ChIP-seq, etc.), biological networks, and gene regulatory networks. Studies must be focused on answering biological questions related to embryonic development, tissue formation, or cellular differentiation. Machine learning algorithms trained and tested with up-to-date datasets are encouraged as long as they focus on answering a biological question of interest of this Research Topic, or overcoming some known problem in dealing with this kind of data.
• The description of new gene regulatory networks related to development, tissue formation, or differentiation. The manuscript must bring new algorithms and/or sound integrative approaches;
• Toxicological effects of different substances and compounds on development. Same as other topics, studies must apply integrative approaches to explore their data further;
• New protein structures, and molecular and quantum dynamics simulations are welcome as long as they report and/or analyze proteins related to this research topic's subjects of interest. A combination of new structures and simulations are highly favored;
• Combinatorial analysis of different types or a vast amount of expression data types is considered, as long as they are combined with integrative approaches.
Please note that we will not consider works describing new SNPs or SNVs, nor will we accept works that describe new phenotypes but do not address their molecular mechanisms. Descriptions of new algorithms that do not attempt to address a biological question will not be considered either.
Keywords: Systems Biology, Developmental Biology, Differentiation, Bioinformatics, Machine Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
