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.