Single-cell control of microbial selection and evolution

Feedback control is a fundamental engineering principle that has been used successfully for decades to improve the robustness and performance of engineered and natural systems, and to drive their output to desirable set-points. Examples abound, ranging from Watt's fly-ball governor to medical life-support systems to fly-by-wire planes.

With advances in computation and instrumentation, as well as new theoretical frameworks, the field is now moving towards distributed, data-driven, real-time optimal feedback control solutions. The ultimate aim is to address the challenge of controlling systems with significant uncertainty - both in their modeling, as well as in the environment they need to operate. Indeed, systems of this type are becoming more and more prevalent, and autonomous systems solutions are being sought across technology and biomedicine. The development of new theory, tools, and instrumentation for these challenges creates new opportunities, and new horizons, with significant impact in areas where feedback control has not had widespread application yet.

One such area is biological evolution. Defined as the change in inherited characteristics over successive generations of a species, it is a slow optimisation process that over time (sometimes millions of years) naturally improves traits and species fitness. Directed evolution - a cyclic process of gene diversification, screening, and selection - has had significant impact on biotechnology in the past few years, but this is largely a passive, uncontrolled process. The benefits of dynamically steering evolution so as to improve single-cell designs in a predictable fashion autonomously would be hugely significant - with applications across biotechnology and beyond. However this has not been attempted in closed loop before, not least because the experimental paradigm and feedback control frameworks required to realise this vision are currently missing.

In this new horizons project we will combine advanced instrumentation with feedback control theory to develop a first-of-its-kind platform for closed-loop directed evolution. In particular, we will create a robotic microscope platform to monitor the behaviour of ~1 million bacterial cells as they grow continuously, confined in a microfluidic chip for long periods of time. Continuous imaging of the population will be used to quantify each cell's performance, with this information driving a feedback controller that will in turn decide which cells to propagate and which not, in order to achieve a target distribution of behaviours over time. Feedback control at this scale has not been attempted before, and this application area will push the development of new theory and algorithms.

Indeed, our controlled evolution platform will advance the boundaries of what is possible right now both from the standpoint of instrumentation and control, as well as in terms of understanding and manipulating evolution. The development of this robotic control approach is motivated by the need for real-time, accurate actuation for directed evolution and is facilitated by nascent microscopy and image processing technologies. Moreover, the development of the theory is challenged by the size of the system to be controlled (millions of states) as well as uncertainties involved in modeling and observing the stochastic evolutionary process. On the whole, this high-risk high-reward project will unlock a number of biotechnological applications, as well as opening new research opportunities in biological evolution and feedback control.