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Details of Grant 

EPSRC Reference: EP/R035350/1
Title: McSynC: in vivo automatic Model calibration of Synthetic Circuits components
Principal Investigator: Menolascina, Dr F
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: New Investigator Award
Starts: 01 August 2018 Ends: 31 July 2020 Value (£): 169,368
EPSRC Research Topic Classifications:
Synthetic biology
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
11 Apr 2018 Engineering Prioritisation Panel Meeting 11 and 12 April 2018 Announced
Summary on Grant Application Form
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.
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