Data-based optimal control of synthetic biology gene circuits

Synthetic Biology aims at the engineering of biological systems. Its most prominent application is the rational modification or (re-)design of living organisms, ideally in a way akin to the engineering of man-made devices, for their efficient use in sectors such as energy, biomedicine, drug production and food technology. The availability of control mechanisms that can ensure robust and optimal operation of engineered systems is one of the key factors behind the tremendous advances in engineering fields such as transportation, industrial production and energy. However, in the case of engineered biosystems, their accurate control must typically overcome two important hurdles: uncertainty and noise. Uncertainty arises from a high number of components that interact in a nonlinear (and often unknown) manner, and makes it often extremely hard to build accurate mathematical models of their behaviour. Noise, on the other hand, is ubiquitous in cellular systems since the environmental conditions in which they operate typically vary unpredictably and gene expression is inherently a stochastic process.

In this research, we investigate the possibility of automatically learning to optimally control synthetic biology gene networks from input-output data collected from these gene networks, i.e. without using a mathematical model built a priori. In particular, we will develop algorithms that allow computer-based systems to autonomously learn how to vary the inputs of a given system so as to optimise its performance defined in terms of the time evolution of its measured outputs. The control strategies learned by our methods will take into account noise and uncertainties in the data and will be developed to be robust with respect to these. Such data-based strategies are analogous to, for example, the way we drive our cars: without any a priori mathematical model of the car behaviour on the road, we can effectively learn how and when to steer, accelerate and break (inputs) based on our observations of the car's position and velocity on the road (outputs) so as to, for example, minimise our lap time around an unknown track using appropriate input scheduling strategies.

The algorithms we will develop will allow users to define the desired behaviour and performance objectives and will compute input-scheduling strategies that allow these objectives to be satisfied. The project will build on methods that I have developed and successfully applied to the optimal control of nonlinear systems in noisy environments, e.g., my work on data-based optimal drug-scheduling for HIV infected patients. The use of such purely data-based optimal control methods is particularly important in synthetic biology applications where the system to be controlled is typically poorly characterised and model uncertainties prevail, yet large amount of high-throughput input-output data are available or can be extracted. To showcase the potential of these computational techniques, we will develop data-based methods to optimally control two landmark synthetic biomodules: the light-inducible genetic toggle switch, and the light-inducible generalised repressilator, both of which are currently under implementation in my host Department.

* Impact on Society: One of the main goal of Synthetic Biology is the engineering of biology. Its considerable anticipated impact in biotechnology, energy production, food technology, biomedicine and society in general has been acknowledged worldwide by UK, EU and US policy makers.

The availability of control mechanisms that can ensure robust and optimal operation of controlled systems is one of the key factors behind the tremendous advances in engineering fields like transportation, industrial production and energy. However, unlike for these traditional engineering technologies, we are still unable to externally control synthetic biology circuits so as to optimise their behaviour in the presence of uncertainty. This inability is a major bottleneck in the development of Synthetic Biology as a true engineering discipline.

This project will narrow this gap and bring Synthetic Biology technologies one step closer to real-world applications. It will work at a foundational level by delivering a computational framework that can be adopted in a broad range of areas. The project will have an important impact on the development of Synthetic Biology and will lay the groundwork for future applications that require optimal control of gene networks. This will open up avenues for new research and development for the years to come.

* Impact on Knowledge: There is a recent trend in the Control Engineering community to focus on problems from the biosciences. This tendency is having an enormous impact in Control Theory and Biology, by providing a new class of theoretical problems and practical solutions motivated by the inherent complexity of intracellular systems. This project is in line with this tendency and lies at the interface between Control Theory and biological systems. It will stimulate the development of new data-based optimal control methods and will provide a major emphasis on the role of Control Engineering in biological applications.

* Impact on Economy: The biotechnology industry constitutes the main target for economic impact. With large amounts of money spent in the efficient development of bacteria capable of efficiently producing biofuels, commodity chemicals, pharmaceuticals, etc., the economic impact of efficient and robust optimal control solutions can be very large. The increasing availability of large quantities of input-output data opens avenues for the systematic investigation of methods capable of inferring input-scheduling strategies allowing to obtain an optimal dynamical behaviour at the observed outputs of the considered systems. The multidisciplinary knowledge developed through this project will thus allow us to contribute to the excellence of control engineering and synthetic biology research in the UK and worldwide.

* Impact on research communities: All the participants involved in the project will greatly benefit from the project. The RA will further develop his or her expertise in several areas of optimal control, machine learning and cellular biology and will be directly exposed to leading research in Synthetic Biology. The project will also provide the RA with the opportunity to deepen his or her understanding of experimental techniques used to measure input-output data that will be used in the project, and of the details relating to several synthetic biology gene networks chosen as benchmark systems in this project. My colleagues at the Department of Bioengineering and the Centre for Synthetic Biology and Innovation at Imperial College and, more broadly, the Synthetic Biology and Control Engineering communities, will also benefit from the results of this project through the engineering methods that this project will provide them with. In particular, this work will serve as a pioneering step to showcase the potential and usefulness of directly using input-output data to infer optimal control strategies for Synthetic Biology systems.