Title:Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications
VOLUME: 19 ISSUE: 8
Author(s):Kok Siong Ang, Meiyappan Lakshmanan, Na-Rae Lee and Dong-Yup Lee*
Affiliation:Bioprocessing Technology Institute (BTI), A*STAR, Singapore 138668, Bioprocessing Technology Institute (BTI), A*STAR, Singapore 138668, Department of Chemical and Biomolecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117585, Bioprocessing Technology Institute (BTI), A*STAR, Singapore 138668
Keywords:Microbial communities, Metabolism, Community modeling, Genome-scale metabolic models, Flux balance analysis,
Kinetic models.
Abstract:In nature, microbes do not exist in isolation but co-exist in a variety of ecological and biological
environments and on various host organisms. Due to their close proximity, these microbes interact
among themselves, and also with the hosts in both positive and negative manners. Moreover,
these interactions may modulate dynamically upon external stimulus as well as internal community
changes. This demands systematic techniques such as mathematical modeling to understand the intrinsic
community behavior. Here, we reviewed various approaches for metabolic modeling of microbial
communities. If detailed species-specific information is available, segregated models of individual organisms
can be constructed and connected via metabolite exchanges; otherwise, the community may
be represented as a lumped network of metabolic reactions. The constructed models can then be simulated
to help fill knowledge gaps, and generate testable hypotheses for designing new experiments.
More importantly, such community models have been developed to study microbial interactions in
various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment
and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights
into the natural and synthetic microbial communities, and design interventions to achieve specific
goals. Finally, potential directions for future development in metabolic modeling of microbial communities
were also discussed.
