Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

Many disciplines rely on testing combinations of compounds,
materials,
proteins, or bacterial species to drive scientific discovery. It is
time-consuming and expensive to determine experimentally, via trial-and-error
or random selection approaches, which of the many possible combinations
will lead to desirable outcomes. Hence, there is a pressing need for
more rational and efficient experimental design approaches to reduce
experimental effort. In this work, we demonstrate the potential of
machine learning methods for the in silico selection of promising
co-culture combinations in the application of bioaugmentation. We
use the example of pollutant removal in drinking water treatment plants,
which can be achieved using co-cultures of a specialized pollutant
degrader with combinations of bacterial isolates. To reduce the experimental
effort needed to discover high-performing combinations, we propose
a data-driven experimental design. Based on a dataset of mineralization
performance for all pairs of 13 bacterial species co-cultured with
MSH1, we built a Gaussian process regression model to predict the
Gompertz mineralization parameters of the co-cultures of two and three
species, based on the single-strain parameters. We subsequently used
this model in a Bayesian optimization scheme to suggest potentially
high-performing combinations of bacteria. We achieved good performance
with this approach, both for predicting mineralization parameters
and for selecting effective co-cultures, despite the limited dataset.
As a novel application of Bayesian optimization in bioremediation,
this experimental design approach has promising applications for highlighting
co-culture combinations for in vitro testing in various settings,
to lessen the experimental burden and perform more targeted screenings.