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Kirsten Jensen

Researcher at Imperial College London

Publications -  28
Citations -  2068

Kirsten Jensen is an academic researcher from Imperial College London. The author has contributed to research in topics: Nuclear protein & Promyelocytic leukemia protein. The author has an hindex of 16, co-authored 26 publications receiving 1836 citations. Previous affiliations of Kirsten Jensen include Heinrich Pette Institute.

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Journal ArticleDOI

PML protein isoforms and the RBCC/TRIM motif

TL;DR: The data on the known PML isoforms and splice variants are summarized and a new unifying nomenclature is presented, suggesting that these sequences are indispensable for function, but differ in their C-terminal sequences.
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Regulation of p53 activity in nuclear bodies by a specific PML isoform

TL;DR: The results indicate the existence of a cross‐talk between PML‐ and p53‐dependent growth suppression pathways, implying an important role for NBs and their resident proteins as modulators of p53 functions.
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Validation of an entirely in vitro approach for rapid prototyping of DNA regulatory elements for synthetic biology

TL;DR: A rapid and completely in vitro method to generate DNA templates for cell-free systems, bypassing the need for DNA template generation and amplification from living cells is devised, providing a valuable tool for rapidly prototyping libraries of DNA regulatory elements for synthetic biology.
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Engineering control of bacterial cellulose production using a genetic toolkit and a new cellulose-producing strain.

TL;DR: A bacterial strain that is readily amenable to genetic engineering and produces high quantities of bacterial cellulose in low-cost media is described and sequenced and genetic circuits that enable functionalization and patterning of heterologous gene expression within the cellulose matrix are demonstrated.
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Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

TL;DR: A rapid and automated methodology for characterizing new DNA parts from a nonmodel bacterium using cell-free transcription–translation using Bayesian parameter inference and a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions is presented.