We coexist with a vast number of microbes—our microbiota—that live in and on our bodies, and play an important role in human physiology and diseases. Propelled by metagenomics and next-generation DNA sequencing technologies, many scientific advances have been made through the work of large-scale, consortium-driven metagenomic projects. Despite these advances, there are still many fundamental questions regarding the dynamics and control of microbiota to be addressed. Indeed, it is well established that human-associated microbes form a very complex and dynamic ecosystem, which can be altered by drastic diet change, medical interventions, and many other factors. The alterability of our microbiome offers opportunities for practical microbiome-based therapies, e.g., fecal microbiota transplantation and probiotic administration, to restore or maintain our healthy microbiota. Yet, the complex structure and dynamics of the underlying ecosystem render the quantitative study of microbiome-based therapies extremely difficult. In this talk, I will discuss our recent theoretical progress on controlling human microbiota [1-6].

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References: 

[1] Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, Liu Y-Y. Universality of Human Microbial Dynamics. Nature 2016;534:259-262.

[2] Gibson TE, Bashan A, Cao H-T, Weiss ST, Liu Y-Y. On the Origins and Control of Community Types in the Human Microbiome. PLoS Computational Biology 2016;12 (2):e1004688.

[3] Cao H-T, Gibson TE, Bashan A, Liu Y-Y. Inferring Human Microbial Dynamics from Temporal Metagenomics Data: Pitfalls and Lessons. BioEssays 2017;39(2):1600188.

[4] Xiao Y, Angulo MT, Friedman J, Waldor MK, Weiss ST, Liu Y-Y. Mapping the ecological networks of microbial communities from steady-state data. Nature Communications (in press); bioRxiv: https://doi.org/10.1101/150649 

[5] Chen Y, Angulo MT, Liu Y-Y. Revealing complex ecological dynamics via symbolic regression. bioRxiv: https://doi.org/10.1101/074617 [6] Angulo MT, Moog CH, Liu Y-Y. Controlling microbial communities: a theoretical framework. bioRxiv: https://doi.org/10.1101/149765