miTuner - a kit for microRNA based gene expression tuning in mammalian cells

The purpose of this RFC is to introduce a modular expression tuning kit for use in mammalian cells. The kit enables the regulation of the gene expression of any gene of interest (GOI) based on synthetic microRNAs, endogenous microRNAs or a combination of both

miMeasure – a standard for miRNA binding site characterization in mammalian cells

This RFC proposes a standard for the quantitative characterization of miRNA binding sites (miRNA-BS) in mammalian cells. The miMeasure standard introduces a ready-to-use standard measurement plasmid (pSMB_miMeasure, BBa_K337049) enabling rapid experimental characterization of any miRNA-BS of choice. We recommend a new standard unit, RKDU (relative knock-down unit) to describe the knock-down efficiency of a miRNA-BS in a specific cell type. pSMB_miMeasure allows for an easy and fast measurement of RKDU while providing effective normalization against variance stemming from differences in transfection efficiency and from other sources

Dynamics and feedback loops in the transforming growth factor beta signaling pathway.

Transforming growth factor beta (TGF-beta) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-beta target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments