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Fahim Farzadfard

Researcher at Massachusetts Institute of Technology

Publications -  24
Citations -  1237

Fahim Farzadfard is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & CRISPR. The author has an hindex of 11, co-authored 24 publications receiving 962 citations. Previous affiliations of Fahim Farzadfard include University of Tehran.

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Tunable and Multifunctional Eukaryotic Transcription Factors Based on CRISPR/Cas

TL;DR: In this article, a CRISPR/Cas system of Streptococcus pyogenes can be programmed to direct both activation and repression to natural and artificial eukaryotic promoters through the simple engineering of guide RNAs with base-pairing complementarity to target DNA sites.
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Genomically encoded analog memory with precise in vivo DNA writing in living cell populations

TL;DR: A scalable platform that uses genomic DNA for analog, rewritable, and flexible memory distributed across living cell populations and the ability to regulate the generation of arbitrary targeted mutations with other gene-editing technologies should enable genomically encoded memory in additional organisms.

Genomically encoded analog memory with precise in vivo DNA writing in living cell populations

TL;DR: In this paper, the authors use the DNA of living cell populations as genomic tape recorders for the analog and distributed recording of long-term event histories for environmental and biomedical applications, biological state machines, and enhanced genome engineering strategies.
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Emerging applications for DNA writers and molecular recorders.

TL;DR: This Review surveys these technological advances, outlines their prospects and emerging applications, and discusses the features and current limitations of these technologies for building various genetic circuits for processing and recording information in living cells.
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Single-Nucleotide-Resolution Computing and Memory in Living Cells

TL;DR: DOMINO is a robust and scalable platform for encoding logic and memory in bacterial and eukaryotic cells and will lay the foundation for building robust and sophisticated computation-and-memory gene circuits for numerous biotechnological and biomedical applications.