Optogenetic control of living cells and its applications

Abstract

Fueled by technological advances in DNA sequencing, laboratory automation, and a growing repository of parts, synthetic biology is yielding dividends; the engineering of biological circuits is leading to successes in a broad range of areas, such as the bioproduction of novel compounds, or the therapeutic treatment of disease. However, a weak spot of most designs is their lack of robustness to environmental fluctuations or changes occurring in the host organism. One approach to tackle this issue is through feedback regulation; maintaining key nodes in the circuits constant by regularly measuring their abundance, comparing it with the desired value, and applying a corrective action. Circuits for feedback regulation can be encoded inside the cell itself in vivo control), or implemented externally (in silico control) by means of a computer able to influence cellular behavior. In this thesis, it is the latter that is on the spotlight; light-responsive cells were interfaced with platforms able to measure cellular behavior in real-time, and provide light inputs in order to achieve desired behaviors. In this manner, the strengths of in silico feedback control were capitalized to reduce variability in experimental outcome, precisely tune cellular processes and study the cells being regulated. The first work presented in the thesis is the construction of an automated platform for optogenetic regulation of bacterial populations. Using this experimental platform, we precisely regulated gene expression in Escherichia coli and showed how disturbances to the cells such as changes in media or temperature could be corrected by feedback control. In contrast, when no feedback mechanism was employed large differences in outcome were observed. Furthermore, using feedback we could control cellular growth, a process tightly regulated by the cell itself. In a second step, we built an experimental platform for single-cell optogenetic feedback control of Saccharomyces cerevisiae. Stochastic transcription was observed in real-time, and probing individual cells with optogenetic inputs revealed insights about this fundamental biological phenomenon. In addition, the large cell-to-cell variability inherent to the process could effectively be reduced by tuning the inputs sent to each cell. Finally, in the last work present in the thesis, the platform for single-cell feedback regulation was repurposed for the testing and characterization of biomolecular (in vivo) controllers. Biomolecular control motifs from the literature were implemented as stochastic chemical reaction networks in a computer. The biomolecular controllers were then interfaced with living cells by means of single-cell measurements and actuation. From these experiments, we could derive insights into conditions for adequate biomolecular performance, and pitfalls to avoid during their biological implementation.