Rational Design and Biological Complexity: Quantitative Measurement and Prediction of Synthetic Circuits Using Plant Protoplasts

Plant synthetic biology is of great interest for development of sustainable green technologies to meet basic needs. A key step in the rational design of synthetic networks is the quantitative characterization of components to enable predictive modeling of the synthetic network properties. However this poses special difficulties for plants since stably transforming plants is time consuming and can take months. An alternative strategy is the use of transient protoplast assays for quantitative testing of components. We developed an experimental test bed for characterizing externally inducible repressors using protoplasts in 96-well plates, but found that transient protoplast assays show significant experimental variability that makes direct quantitative comparisons yield incorrect results. I will discuss some of the sources of variability as well as a mathematical model we developed and then used to normalize the data and make quantitative comparisons between different inducible repressors. Despite some remaining uncertainties we showed that protoplast assays could approximately predict quantitative properties of synthetic circuits in stably transformed plants. We tested hundreds of repressible promoters built by the Medford lab, and carried out a statistical analysis of the quantitative data to uncover design principles for building synthetic inducible repressors in plants (Nature Methods, v13, pp94–100, (2016)). I will also discuss our current attempts to use flow cytometry for protoplast experiments. The next step in rational design involves using the numbers we obtain from our test-bed to predict the properties of larger networks that are assembled from these quantitatively characterized components. In our case we were interested in finding best combinations for a genetic toggle switch in a plant. An important question that arises here is how well in silico predictions can be expected to match experiments in complex organisms. I will discuss statistical methods we use for making such predictions as well as some of the complexities of real biological systems that pose challenges that need to be overcome.