Microorganisms start colonizing human bodies during or shortly after birth, initiating a lifelong process of developing a highly-complex ecosystem of interactions with the host and each other. Our microbiomes are a vital part of our body that performs various critical functions for human health, including protection against pathogens, regulation of the immune system, and metabolism of otherwise-indigestible polysaccharides and other substances (e.g., drugs). Maintaining a desirable ecological balance of populations in the human microbiome is important because alterations in their composition and function (i.e., dysbiosis) are linked with detrimental physiological and psychological impacts, and result in a wide array of disease conditions.
Rapid advancement of high-throughput, culture-independent analytical technologies has led to a large body of experimental data, greatly facilitating the study of the human microbiome. Sequencing 16S rRNA genes and metagenomes allows us to identify a community’s membership, structure, and genetic potential. Transcriptomics, proteomics, and metabolomics provide key information on the functional activities occurring within the microbiome. While these data streams have contributed to expand our understanding of the bacterial role in health and disease, many of the fundamental questions still remain unanswered: for example, “What principles govern the response of the human microbiome to the host genomes and environmental conditions?”, and “How do changes in bacterial populations and interactions modulate community-level functions to affect human wellness?”
Addressing these questions requires a mechanistic understanding of the interplay among the microbiota inhabiting diverse human microbiomes as well as host and environmental factors. Predictive computational modeling is a critical tool for achieving this goal. Through the synergistic integration of multi-omics data mentioned above, mathematical models can provide a fundamental understanding of ecological dynamics of microbial communities, contribute to hypothesis generation and experimental design, and generate novel insights into the causal relationships between the microbiome and host in health and disease. Over the last decade, we have seen a significant growth in computational science in this regard, including both data-driven and mechanism-based approaches along the top-down and bottom-up directions.
This Research Topic invites experts in the related fields to contribute original research articles, methods, perspective, opinion, hypothesis and theory, as well as reviews, which can provide a fundamental basis for advancing the quantitative modeling of human microbiota and stimulate our continued efforts to understand their relationships with the host and environment.
Microorganisms start colonizing human bodies during or shortly after birth, initiating a lifelong process of developing a highly-complex ecosystem of interactions with the host and each other. Our microbiomes are a vital part of our body that performs various critical functions for human health, including protection against pathogens, regulation of the immune system, and metabolism of otherwise-indigestible polysaccharides and other substances (e.g., drugs). Maintaining a desirable ecological balance of populations in the human microbiome is important because alterations in their composition and function (i.e., dysbiosis) are linked with detrimental physiological and psychological impacts, and result in a wide array of disease conditions.
Rapid advancement of high-throughput, culture-independent analytical technologies has led to a large body of experimental data, greatly facilitating the study of the human microbiome. Sequencing 16S rRNA genes and metagenomes allows us to identify a community’s membership, structure, and genetic potential. Transcriptomics, proteomics, and metabolomics provide key information on the functional activities occurring within the microbiome. While these data streams have contributed to expand our understanding of the bacterial role in health and disease, many of the fundamental questions still remain unanswered: for example, “What principles govern the response of the human microbiome to the host genomes and environmental conditions?”, and “How do changes in bacterial populations and interactions modulate community-level functions to affect human wellness?”
Addressing these questions requires a mechanistic understanding of the interplay among the microbiota inhabiting diverse human microbiomes as well as host and environmental factors. Predictive computational modeling is a critical tool for achieving this goal. Through the synergistic integration of multi-omics data mentioned above, mathematical models can provide a fundamental understanding of ecological dynamics of microbial communities, contribute to hypothesis generation and experimental design, and generate novel insights into the causal relationships between the microbiome and host in health and disease. Over the last decade, we have seen a significant growth in computational science in this regard, including both data-driven and mechanism-based approaches along the top-down and bottom-up directions.
This Research Topic invites experts in the related fields to contribute original research articles, methods, perspective, opinion, hypothesis and theory, as well as reviews, which can provide a fundamental basis for advancing the quantitative modeling of human microbiota and stimulate our continued efforts to understand their relationships with the host and environment.