Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities
•Multiple high-throughput methods for studying the human microbiome are available.•System-level models are needed to gain a predictive understanding of the microbiome.•Preliminary modeling efforts show promise but many challenges remain.•Interactions between species, across the community, and with a host can be modeled.•Integration of multiple approaches is required to construct a comprehensive model.
The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically relevant and ecologically relevant insights from the rich trove of data now available.
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Journal: Current Opinion in Biotechnology - Volume 24, Issue 4, August 2013, Pages 810–820