Discussion and Outlook
The field of synthetic biology has grown remarkably in the past two
decades, and it has made substantial progress in engineering gene
regulatory and metabolic networks in microorganisms. In recent years the
field has put a spotlight on engineering microbial consortia
(Brenner et al.,
2008), with a focus again on their composition and genetic make up.
Notwithstanding the many successful examples we cite above, and the many
others we have surely missed, there is much room to be explored in
engineering the environment where microorganisms grow. Among the most
promising directions we would like to highlight is the development of
evolutionary engineering approaches. While the examples we discuss above
were executed in parallel, evolutionary engineering of the environment
can also be implemented in time, where multiple environmental changes
are introduced serially and the best environment is retained. The
development of highly controllable milifluidic devices for continuous
culture of microorganisms offer a promising platform for identifying
optimal culture conditions for organisms and communities
(Mancuso et al., 2021;
Wong et al., 2018).
After a slow start, the field of artificial community-level selection is
experiencing growing attention
(Blouin et al., 2015;
Chang et al., 2021; Doulcier et al., 2020, 2020; Panke-Buisse et al.,
2015; Swenson et al., 2000; Vessman et al., 2023; Williams and Lenton,
2007; Xie et al., 2019; Xie and Shou, 2021). There exist obvious
parallels between the process of optimizing the composition of an
inoculum in a fixed environment, and that of optimizing an environment
for a fixed inoculant. These may lead to a dialog between both fields,
allowing the transfer of successful methodologies from one to another.
Encouragingly, a wealth of novel evolutionary algorithms are being
developed for the directed evolution of microbial communities
(Chang et al., 2021;
Vessman et al., 2023; Xie et al., 2019). These could be fruitful when
applied to finding optimal combinations of environmental factors.
In a bottom-up manner, genome-scale metabolic models have been used for
some time to rationally manipulate microbial interactions and to predict
environments where microorganisms may coexist
(Harcombe et al.,
2014; Klitgord and Segrè, 2010), and they have been used to find
culture conditions that optimize the growth of single strains or the
production of target molecular products (e.g.
(Swayambhu et al.,
2020)). Constraint based models are being developed to quantitatively
predict the behavior of microbial consortia (e.g. see
(Heinken et al.,
2021) and references therein), paving the way for their application to
finding optimal environments.
Rather than being two parallel pursuits, bottom-up and top-down
approaches can actually benefit from one another. By gaining a deeper
understanding of the topology of the map (i.e. the response surface)
between environment and function, researchers will be better equipped to
design evolutionary algorithms that are capable of efficiently
navigating those maps and finding optimal culture conditions. We hope
that our review will stimulate efforts on this front.