Abstract
The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.
Highlights
Many synthetic gene circuits fall into the category of information-processing systems that convert molecular inputs to molecular outputs according to a specific relationship [1], often called a “program”
The method is extensively validated with forward-simulated flow cytometry data from stable and transient transfections, with up to seven different circuits
The results show excellent correlation between the I/O behavior extracted by Peakfinder algorithm for flow cytometry data (PFAFF) from simulated transient transfection data, and the data simulated for stably integrated circuit
Summary
Many synthetic gene circuits fall into the category of information-processing systems that convert molecular inputs to molecular outputs according to a specific relationship [1], often called a “program”. Direct characterization is possible when both the input(s) and the output(s) can be measured simultaneously in single cells. It has emerged that the output forms a distribution at a single cell level for each input [8,9,10], resulting in a two-dimensional probability distribution for the entire I/O relationship, rather than a curve, due to cell-to-cell variation in parameter values. After averaging, these noisy data sets usually collapse to Hill functions or to multimodal, two-value functions [11]
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