Systems biology adopting integrative omics approaches aims to understand the biology of complex diseases in a holistic framework. Rapid development of omics techniques like genomics, transcriptomics, proteomics, metabolomics, lipidomics and so on, along with computational/bioinformatics methods and tools is advancing our understanding of biological systems in exquisite molecular details. Multi-omics and integrative approaches are increasingly being used to study biological systems in health and to unearth hitherto unknown molecular players in many complex genetic diseases including: human microbiome infection and immunological diseases, host-pathogen interactions, as well as metabolic disorders like diabetes and cardiovascular diseases. Combining the power of the so-called “hypothesis-free” omics methods and “hypothesis-driven” molecular biology methods can synergistically push the frontiers of biological knowledge in these research areas.
Omics technologies generate gigantic datasets that can lead to an analysis bottleneck. The computational/bioinformatics tools that help leverage this data for generating knowledge about biological systems are an integral part of systems biology. Through this Research Topic we invite articles that help advance the understanding of biology at the systems level, providing an overview of the methodological advances, the computational tools and the pipelines that help us dive deeper into complex diseases like metabolic, genetic and infectious diseases.
While high –throughput methods have advanced the state-of the art continuously from the past few decades, the big-data avalanche has made the integration approaches more challenging. Such integrative analytical approaches will drive the understanding of diseases and host pathogen-interactions at a systems level. We welcome submissions covering the following subtopics:
• Omics and multi-omics methods to study metabolism of complex diseases like diabetes, cardiovascular disease and other complex metabolic diseases, as well as host-pathogen interactions in infectious diseases
• Genomics, transcriptomics and proteogenomics insights into diseases and genome annotation/re-annotation
• Proteomics, posttranslational modifications (PTMs) and protein-protein interaction (PPI), biological pathways, networks and network rewiring to study diseases
• Metabolomics and lipidomics to understand metabolic rewiring in diseases
• Genomics, transcriptomics, metagenomics and meta-proteomics approaches to study the human microbiome
• Network medicine and systems biology from big data analysis
• Big data handling, storage, warehousing, integration strategies and database resources
• ML/AI applications for omics data analysis and integration
• Using omics approaches to evaluate therapeutic interventions
Systems biology adopting integrative omics approaches aims to understand the biology of complex diseases in a holistic framework. Rapid development of omics techniques like genomics, transcriptomics, proteomics, metabolomics, lipidomics and so on, along with computational/bioinformatics methods and tools is advancing our understanding of biological systems in exquisite molecular details. Multi-omics and integrative approaches are increasingly being used to study biological systems in health and to unearth hitherto unknown molecular players in many complex genetic diseases including: human microbiome infection and immunological diseases, host-pathogen interactions, as well as metabolic disorders like diabetes and cardiovascular diseases. Combining the power of the so-called “hypothesis-free” omics methods and “hypothesis-driven” molecular biology methods can synergistically push the frontiers of biological knowledge in these research areas.
Omics technologies generate gigantic datasets that can lead to an analysis bottleneck. The computational/bioinformatics tools that help leverage this data for generating knowledge about biological systems are an integral part of systems biology. Through this Research Topic we invite articles that help advance the understanding of biology at the systems level, providing an overview of the methodological advances, the computational tools and the pipelines that help us dive deeper into complex diseases like metabolic, genetic and infectious diseases.
While high –throughput methods have advanced the state-of the art continuously from the past few decades, the big-data avalanche has made the integration approaches more challenging. Such integrative analytical approaches will drive the understanding of diseases and host pathogen-interactions at a systems level. We welcome submissions covering the following subtopics:
• Omics and multi-omics methods to study metabolism of complex diseases like diabetes, cardiovascular disease and other complex metabolic diseases, as well as host-pathogen interactions in infectious diseases
• Genomics, transcriptomics and proteogenomics insights into diseases and genome annotation/re-annotation
• Proteomics, posttranslational modifications (PTMs) and protein-protein interaction (PPI), biological pathways, networks and network rewiring to study diseases
• Metabolomics and lipidomics to understand metabolic rewiring in diseases
• Genomics, transcriptomics, metagenomics and meta-proteomics approaches to study the human microbiome
• Network medicine and systems biology from big data analysis
• Big data handling, storage, warehousing, integration strategies and database resources
• ML/AI applications for omics data analysis and integration
• Using omics approaches to evaluate therapeutic interventions
