@article{agora,
  title={Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota},
  author={Magn{\'u}sd{\'o}ttir, Stefan{\'\i}a and Heinken, Almut and Kutt, Laura and Ravcheev, Dmitry A and Bauer, Eugen and Noronha, Alberto and Greenhalgh, Kacy and J{\"a}ger, Christian and Baginska, Joanna and Wilmes, Paul and others},
  journal={Nature biotechnology},
  volume={35},
  number={1},
  pages={81},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{agora_reply,
  title={Reply to" Challenges in modeling the human gut microbiome"},
  author={Magnusdottir, Stefania and Heinken, Almut and Fleming, Ronan MT and Thiele, Ines},
  journal={Nature biotechnology},
  volume={36},
  number={8},
  pages={686--692},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{micom,
  author = {Diener, Christian and Gibbons, Sean M. and Resendis-Antonio, Osbaldo},
	editor = {Chia, Nicholas},
	title = {MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota},
	volume = {5},
	number = {1},
	elocation-id = {e00606-19},
	year = {2020},
	doi = {10.1128/mSystems.00606-19},
	publisher = {American Society for Microbiology Journals},
	abstract = {Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn{\textquoteright}s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom.IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.},
	URL = {https://msystems.asm.org/content/5/1/e00606-19},
	eprint = {https://msystems.asm.org/content/5/1/e00606-19.full.pdf},
	journal = {mSystems}
}

