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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