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Levner, D

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Levner

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Levner, D

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2012 - 20143

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Author's Publications: search.filters.isAuthorOfPublication.e5c8a386-9f59-4e6f-bf7e-c64ad3cc3c78

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    Barcoding cells using cell-surface programmable DNA-binding domains
    (2013) Mali, Prashant; Aach, John; Lee, Jehyuk; Levner, D; Nip, Lisa; Church, George
    We develop here a novel approach to barcode large numbers of cells through cell-surface expression of programmable zinc-finger DNA-binding domains (sZFs). We show sZFs enable double-stranded DNA to sequence-specifically label living cells, and also develop a sequential tagging approach to in situ image >3 cell types using just 3 fluorophores. Finally we demonstrate their broad versatility through ability to serve as surrogate reporters and facilitate selective cell capture and targeting.
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    Universal computing by DNA origami robots in a living animal
    (2014) Amir, Yaniv; Ben-Ishay, Eldad; Levner, D; Ittah, Shmulik; Abu-Horowitz, Almogit; Bachelet, Ido
    Biological systems are collections of discrete molecular objects that move around and collide with each other. Cells carry out elaborate processes by precisely controlling these collisions, but developing artificial machines that can interface with and control such interactions remains a significant challenge. DNA is a natural substrate for computing and has been used to implement a diverse set of mathematical problems1-3, logic circuits4-6 and robotics7-9. The molecule also naturally interfaces with living systems, and different forms of DNA-based biocomputing have previously been demonstrated10-13. Here we show that DNA origami14-16 can be used to fabricate nanoscale robots that are capable of dynamically interacting with each other17-18 in a living animal. The interactions generate logical outputs, which are relayed to switch molecular payloads on or off. As a proof-of-principle, we use the system to create architectures that emulate various logic gates (AND, OR, XOR, NAND, NOT, CNOT, and a half adder). Following an ex vivo prototyping phase, we successfully employed the DNA origami robots in living cockroaches (Blaberus discoidalis) to control a molecule that targets the cells of the animal.
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    How Accurate Can Genetic Predictions Be?
    (BioMed Central, 2012) Dreyfuss, Jonathan M; Levner, D; Galagan, James E.; Church, George; Ramoni, Marco F
    Background: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.