Single-cell measurements; RNA interference; Synthetic Biology; Systems Biology; Biosensors
LillacciGabriele, BenensonYaakov, KhammashMustafa (2018), Synthetic control systems for high performance gene expression in mammalian cells, in Nucleic Acids Research
, 46(18), 9855-9863.
Dastor Margaux, Schreiber Joerg, Prochazka Laura, Angelici Bartolomeo, Kleinert Jonathan, Klebba Ina, Doshi Jiten, Shen Linling, Benenson Yaakov (2018), A Workflow for In Vivo Evaluation of Candidate Inputs and Outputs for Cell Classifier Gene Circuits, in ACS Synthetic Biology
, 7(2), 474-489.
Prochazka Laura, Benenson Yaakov, Zandstra Peter W. (2017), Synthetic gene circuits and cellular decision-making in human pluripotent stem cells, in Current Opinion in Systems Biology
, 5, 93-103.
Mohammadi Pejman, Beerenwinkel Niko, Benenson Yaakov (2017), Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy, in Cell Systems
, 4(2), 207-218.e14.
Schreiber Joerg, Arter Meret, Lapique Nicolas, Haefliger Benjamin, Benenson Yaakov (2016), Model‐guided combinatorial optimization of complex synthetic gene networks, in Molecular Systems Biology
, 12(12), 899-899.
RNA interference (RNAi) was discovered about two decades ago and since then has become a focus of intense investigation, both due its basic biological importance in controlling gene expression across multiple kingdoms of life and because of its potential importance in disease diagnostics and treatment. RNAi has also formed the basis of an increasing number of synthetic gene circuits, whose applications range from basic biological research to biomedicine. Yet despite enormous progress in our understanding and utilization of RNAi pathway, surprisingly little is known about fine-grained quantitative parameters of this process. For example, a question such as "What is the expected gene knockdown by a specific concentration of a certain microRNA molecule, given the sequence of a microRNA target in this gene's 3'-UTR?" cannot be answered using current knowledge. We contend that measuring and understanding these parameters is key to future advances of basic and applied exploration of RNA interference. This proposal described a series of experiments whose collective aim is to elucidate quantitative features of RNAi in mammalian cells on a population and single-cell level. The key experimental goal is to link the amount of intracellular microRNA molecules, measured in absolute units, to the observed degree of target gene knockdown under different conditions. The experimental variables include the sequence of a microRNA itself, sequence of the microRNA target (ranging from partial to full complementarity and varying the number of target repeats), and the cell type in which the knockdown takes place. Quantitative data will form the basis of a computational model that will recapitulate RNAi pathway with unprecedented degree of precision, capturing both average features and cell-to-cell heterogeneity ("noise"). Ultimately, the data in combination with the model will allow the construction of "universal" genetic microRNA sensors capable of reporting absolute intracellular microRNA levels based on fluorescent readouts. Such easy-to-use, high-throughput sensors will be of immediate importance in both basic biological studies and as a diagnostic tool in the clinic.