  Microbial communities assemble through a complex set of interactions between
microbes and their environment, and the resulting metabolic impact on the host
ecosystem can be profound. Microbial activity is known to impact human health,
plant growth, water quality, and soil carbon storage which has lead to the
development of many approaches and products meant to manipulate the microbiome.
In order to understand, predict, and improve microbial community engineering,
genome-scale modeling techniques have been developed to translate genomic data
into inferred microbial dynamics. However, these techniques rely heavily on
simulation to draw conclusions which may vary with unknown parameters or
initial conditions, rather than more robust qualitative analysis. To better
understand microbial community dynamics using genome-scale modeling, we provide
a tool to investigate the network of interactions between microbes and
environmental metabolites over time.
  Using our previously developed algorithm for simulating microbial communities
from genome-scale metabolic models (GSMs), we infer the set of
microbe-metabolite interactions within a microbial community in a particular
environment. Because these interactions depend on the available environmental
metabolites, we refer to the networks that we infer as \emph{metabolically
contextualized}, and so name our tool MetConSIN: \underline{Met}abolically
\underline{Con}textualized \underline{S}pecies \underline{I}nteraction
\underline{N}etworks.
