McSynC: in vivo automatic Model calibration of Synthetic Circuits components

Synthetic Biology is an emerging engineering discipline with an ambitious goal: empowering scientists with the ability to programme new functions into cells, just like they would do with computers. Despite a booming community and notable successes, however, writing "functioning algorithms" for cells remains extremely time-consuming. This is mostly due to the fact that the building blocks we use to assemble such "algorithms", so-called "parts", rarely behave as expected as their working/dynamics are generally poorly understood. Mathematical models are uniquely suited to address this problem; in engineering, they are routinely used to formally describe systems' behaviour, design/simulate/screen them for performance, and save time bringing only the best solutions to the prototyping stage (Model-Based Systems Engineering). Despite being an engineering discipline, SynBio has so far made limited use of mathematical models, mostly because inferring biological models has been traditionally perceived as expensive and/or difficult.

If SynBio, one of the UK's "8 Great Technologies", is to meet the expectations for a (bio)economy of scale set in the UK Synthetic Biology Strategic Plan we need to accelerate gene circuits prototyping: a "Model-Based Systems Engineering" approach is needed for biological systems; model inference must be simpler, faster and ultimately cheaper. To this aim, I propose to combine Optimal Experimental Design (OED) and microscopy/microfluidics to develop a cyber-physical platform that automates model calibration, i.e. the identification of parameters in a model. Given a part of interest and an initial model, this system will identify in silico the most informative experiment to refine parameter estimates; immediately run such experiment in vivo; use the new experimental data to update the model and design an optimal experiment for the new model, iterating until robust estimates are reached.

Besides automating model calibration, the approach I propose has three main benefits: it allows to obtain, and publicly share, reliable models (a) faster -as fewer experiments are needed if each carries more information, (b) cost-effectively -as microfluidics drastically reduces reagents' use and automation renders human intervention unnecessary, (c) in a reproducible way -as all the data and the steps in the inference are tracked and immediately made publicly available.

As a proof of principle, we will use this approach to fill a gap in yeast SynBio: the lack of a genetic oscillator. Despite the failures in building synthetic oscillators from scratch in S. cerevisiae, a recent study suggested three strategies to turn an existing "switch-like" circuit, IRMA, into an oscillator. Each of these interventions requires parts of the existing circuit to be replaced by new ones with a specific dynamic behaviour. We will use our platform to find the new parts (pEGT2, pHO and pANB1) and guide the gene circuit "refactoring".

In summary, we will:

1. Develop, deploy and test a closed-loop method to automatically infer mathematical models of genetic parts;
2. Build and characterise a library for each of the three parts previously proposed to turn IRMA into an oscillator;
3. Identify, guided by their models, the parts that are the best candidates and use them to refactor the original network;
4. Test the new circuits for oscillations and characterise them.

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. SynBio will benefit from the large set of thoroughly characterised parts we will produce. The Control Engineering and Biodesign 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 host metabolism. 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 eukaryotes. Annotated images and time-series will help researchers in Computer Vision/Machine Learning to develop/test new algorithms to study biological processes. Early industrial adopters will be the secondary beneficiaries. The Edinburgh Genome Foundry plans to use our platform to offer a "quantitative characterisation certificate" for the parts it builds. Cambridge Consultants (CC) will gain new know-how on laboratory automation and expand its presence in SynBio by helping to turn our prototype into commercial products. Synthace will expand the ecosystem of AnthaOS. We will reach out to both academic and industrial stakeholders via two main actions: broadly disseminating our results (seeking continuous feedback from users) and developing a sensible technology exploitation/commercialisation plan with Edinburgh Innovations (EI).

In the medium term (5 to 10 years), this project will impact a whole range of industrial entities that are important for the British bioeconomy ecosystem, as well as international ones. In this time-frame, we will augment our platform with additional functions (e.g. model selection, validation, checking). These efforts will put Synthace, a lively startup spun off of UCL, in a privileged position to lead the much-awaited automation of the Design-Build-Test-Learn cycle. CC, one of the "early movers" in the UK SynBio context, is also expected to leverage on our results to develop fully integrated "automatic model inference" solutions for both public and private laboratories. We will help CC profiling both these types of customers. To this aim we will transfer to them the know-how we will acquire from our interaction with Quantitative Biosciences, an American startup willing to use our platform to streamline their biosensor design process. On the academic side, I expect to be myself a beneficiary of this research as it will give me a 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 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 in the long term is the full automation of the Design-Build-Test-Cycle. This will speed up significantly circuit construction and relieve a fundamental bottleneck of SynBio, unleashing its potential in both academia and industry. 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 a revamped interest for synthetic oscillators recently suggested, such circuits have great potential. In industrial biotechnology, for example, the oscillator we will build can be used to induce periodic flocculation in yeast and streamline the separation of bioproducts from cells in fermenters. To facilitate impacts we will keep seeking opportunities to engage with both academia and industry under the guidance of EI.