An intelligent approach to the automatic characterisation and design of synthetic promoters in mammalian cells

Synthetic Biology (SynBio) is an emerging engineering discipline with an ambitious goal: empowering scientists with the ability to programme new functions into cells, just like we would do with computers. Despite a thriving community and notable successes, however, writing "functioning algorithms" for cells remains extremely time-consuming. This is a roadblock towards the engineering of mammalian cells, an area uniquely positioned to develop potentially groundbreaking therapeutic applications. This translates into high development costs that, in turn, are limiting the pace at which Synthetic Biology progresses towards applications. Model-Based System Engineering (MBSE) is the answer the engineering community found to similar problems and is widely used to streamline manufacturing. In this framework, mathematical models are used to screen candidate designs via simulations and bring to testing only the most promising solutions.

Despite being an engineering discipline, SynBio lacks a MBSE framework. This is largely due to three connected issues: (a) the scarcity of accurate mathematical models of parts (e.g. promoters) in the first place. Such a shortage (b) makes it difficult to "reverse engineer" the connection between the DNA sequence and the kinetics of the transcribed mRNA (e.g. promoter sequence and leakiness of expresion). This means that (c) the inverse "re-design" problem, i.e. finding the optimal DNA sequence of a part, cannot be solved, let alone automatically.

With this fellowship, I aim at filling this gap and develop a "Model-Based Biosystem Engineering" (MBBE) framework to automate the Design-Build-Test-Learn (DBTL) cycle in Synthetic Biology. Given their role in cell and gene therapy, with my team, we will focus on synthetic promoters for mammalian cells. Prompted by the recent successes and challenges of CAR T cells -immune cells engineered to kill cancer cells, we will use the framework to engineer a hypoxia-inducible promoter that optimises a set of criteria we will determine and prioritise with our collaborator Prof. Chen at UCLA.

We will first focus on the development of the MBBE framework; to this aim we will tackle the three issues mentioned above by: (a) developing a high-throughput microfluidic device that allows to infer, with minimum experimental efforts (via Optimal Experimental Design), reliable mathematical models of hundreds of variants of a promoter, (b) using these results to automatically learn/predict gene expression dynamics from promoter sequence via machine learning and (c) combining this prediction scheme with computational optimisation to identify and refine promoter sequences so that they satisfy given specifications and maximise pre-determined objectives.

To develop a hypoxia-inducible promoter, we will start from an initial pool of 600 sequences -designed to cover a fraction of the design space as big as possible, and we will iterate twice over our automatic DBTL loop to finally obtain promoter(s) that can be used to overcome the current limitations of CAR T cells.

Besides automating the DBTL cycle, the approach I propose has three main benefits: it allows to obtain, and publicly share, reliable models (1) faster -as we will use Optimal Experimental Design methods to minimise experimental efforts, (2) cost-effectively -as microfluidics drastically reduces the use of reagents and automation renders human intervention unnecessary; (3) in a reproducible way -as all the data and the steps in the inference are tracked and immediately made publicly available.

I anticipate that this project will encompass a wide range of benefits and beneficiaries. I have structured expected impacts and beneficiaries in time frames: short, medium and long-term.

In the short term (1-5 years) academic research communities will be the primary beneficiary. Mammalian SynBio will benefit from the large set of thoroughly characterised promoters we will produce. The Control Engineering and Bio-Design Automation communities will benefit from the availability of "cheap" yet robust models that can be used, for example, to study how the dynamics of subsystems depend on cellular metabolism (i.e. metabolic burden). Large datasets of sequence/models will provide the Biophysics and Systems Biology communities with novel insights into both network dynamics and how the architecture of promoters determines the kinetics of transcription in mammalian cells. Annotated images and time-series will help researchers in Computer Vision/Machine Learning to develop/test new algorithms to study biological processes. Our industrial partners, Labcyte and Sphere Fluidics, as well as other early industrial adopters will be the secondary beneficiaries. The discussions I had with our partners make me believe that there is an opportunity to develop our results (with particular emphasis on the microfluidic device) into products and license them (e.g. to Sphere Fluidics). To maximise the impact of this project, within the first 5 years, I will spin out a company, "SynQuanta", that will specialise in developing technologies for "quantitative phenotyping" in synthetic biology.

In the medium term (5 to 10 years), this project will impact a wide range of industrial entities that are important for the British bioeconomy. The Edinburgh Genome Foundry is interested in integrating our platform in their assembly pipeline to provide a new "end-to-end" service to its customers. In addition to the impact of the platform, I envision that the hypoxia-inducible promoter we will build has the potential to widen the application of CAR-T cell therapy to non-solid cancers. I expect to be myself a beneficiary of this research as it will give me the chance to establish myself as a leader in the field at the boundary of Control Engineering and SynBio. I will seek to maximise these impacts disseminating my work, liaising with existing and new industrial partners thanks to the support of IBioIC and further integrating my research in training activities.

In the long term (10-50 years), industry and academia will equally benefit from this research. The most important impact foreseen for this project is the full automation of the Design-Build-Test-Cycle in mammalian Synthetic Biology. This will speed up significantly circuit construction and relieve a fundamental bottleneck of SynBio, unleashing its potential in both academia and industry. I anticipate that such developments will make it easier to engineer circuits with a predictable behaviour; these are the key enablers of the next generation of smart therapies for precision medicine. The Social Studies of Science (SSS) community will have the opportunity to study how key aspects of scientists' life (e.g. formation and communication of knowledge) are affected by automation. As we aim at automating "creative" processes (e.g. design/modelling), I am also excited to learn what social scientists will conclude after studying how platforms like ours will change the job market and the education of professionals. To facilitate impacts, we will keep seeking opportunities to engage with both academia and industry under the guidance of EI.