3 Bioinformational versus Biophysical Engineering
The development of synthetic artemisinin has often been used as a case
study of what can be achieved in the field of synthetic biology (Paddon
and Keasling, 2014). However, in truth, it should actually be thought of
as a case study in what can be achieved when yeast is harnessed at its
true potential as a model organism. Synthetic artemisinin stands as an
historical example of biophysical engineering―a refactoring of a yeast’s
metabolic networks to achieve a physical material objective. Biophysical
engineering in yeast platforms has vastly matured since 2013.
High-throughput approaches to combinatorial design (Naseri and Koffas,
2020), massive parallel combinatorial testing (Kehe, 2020) and the
accompanying bioinformatics have fundamentally changed the speed and
scale with which a biological design space can be explored (Dixon et
al., 2021a,b). This is no longer news.
What is news is the next wave of research objectives that will each
benefit from being trialled and optimised within a yeast platform. This
is the realm of bioinformational engineering, where instead of
engineering yeast to achieve physical material objectives, the
objectives are information―that is, using the bio-compute infrastructure
of a yeast colony to sense, surveil and report on the molecular
environment. Although the
traditionally conservative fermented beverage industries, such as the
wine industry (Jagtap et al., 2017; Pretorius, 2000, 2016, 2017a,b;
Pretorius and Bauer, 2002; Pretorius and Høj, 2005), may resist the use
of engineered yeast, it is less likely to be politically impossible for
yeast-based biosensing to be deployed from farm-to-table for monitoring
and improving product quality (Eriksson et al., 2020).
Similarly, as the world continues to experience politically-driven
supply chain shortages across the semiconductor industry (Miller, 2022),
it is going to become increasingly advantageous to devolve computing
power to yeast platforms when those platforms operate as the advanced
sensing nodes in a precision agriculture or precision medicine network.
Just as yeast research is essential today for its contribution to
enhancing the organism’s biophysical output across many varied
industrial applications, so it remains highly likely that yeast will be
integrated into the cyber-biological compute infrastructure of the
coming decades (Botstein and Fink, 2011). In this, the yeast research
community has much to learn from the early days of the quantum computing
industry. Quantum computers are co-processes to classical computers,
they are not going to replace classical computers altogether. Similarly,
there is a once in a generation opportunity to create an entirely new
industrial application for yeast, that as bioinformation co-processes
that can integrate with classical and quantum-classical computer
architectures (Zhirnov, 2018).
Basic science questions abound in a research trajectory that seeks an
end point of integrating yeast biosignals with classical computing. Such
questions range from electron shuttling and bioelectrochemical signal
parsing, through to quorum sensing and intercellular communication in
multi-species single-cell consortia. The opportunities to continue to do
basic yeast science are vast when the work is framed in relation to
government and funder political priorities, and integrated via
multidisciplinary collaboration with government-supported critical
technology domains. For yeast research, this opportunity is easier to
take advantage of than in most scientific disciplines. There has not
been a more exciting time to be in the field.