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Profil korisnika/ce ekelsic
Eric Kelsic
Eric Kelsic
Eric Kelsic
@ekelsic

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Eric Kelsic

@ekelsic

Cofounder & CEO Dyno Therapeutics (@dyno_tx). High-throughput synbio + machine learning for better gene therapy, focused on AAV engineering. We are hiring!

Cambridge, MA
dynotx.com
Vrijeme pridruživanja: rujan 2010.

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    1. Eric Kelsic‏ @ekelsic 29. stu 2019.
      • Prijavi Tweet

      Our AAV paper published in Science on Thanksgiving, 4+ years with @PierceOgdenJ, @samsinai, @geochurch, continuing now at @dyno_tx, here’s how: including twists, turns, surprises, and many people to thank along the way!https://science.sciencemag.org/content/366/6469/1139 …

      56 proslijeđenih tweetova 182 korisnika označavaju da im se sviđa
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      Eric Kelsic‏ @ekelsic 29. stu 2019.
      • Prijavi Tweet

      Our approach generates high-resolution maps of #AAV capsid sequence space, illuminating how capsids work and how to make them better. Here’s a visualization showing tolerance to substitutions (red:beneficial, white:neutral, blue:deleterious, this view isn't even in the paper!)pic.twitter.com/nWUekmzoKP

      22:11 - 29. stu 2019.
      • 9 proslijeđenih tweetova
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      9 proslijeđenih tweetova 41 korisnik označava da mu se sviđa
        1. Novi razgovor
        2. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          I joined @geochurch lab early 2015 aiming to combine 3 technologies for protein engineering: chip-based #DNA synthesis, #NGS & #MachineLearning. I teamed up with the talented @PierceOgdenJ, and @samsinai who joined about a year later to enrich our lives with his ML wisdom.pic.twitter.com/7O3UqEf5oM

          Korisnici Eric Kelsic, Sam Sinai, george church i Pierce
          1 reply 3 proslijeđena tweeta 17 korisnika označava da im se sviđa
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        3. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          (In the course of a epic project like this one, so many people were happy to help in 1e6 different ways, I'll try to acknowledge as many as I can while setting the stage for how and why we set off on this scientific adventure...)

          1 reply 0 proslijeđenih tweetova 4 korisnika označavaju da im se sviđa
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        4. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          We saw amazing potential combining these 3 technologies to study biology & for engineering, inspired by: PhilRomero, @francesarnold, @jbkinney, @segal_eran, @srikosuri, @dbgoodman, @jbloom_lab, @dougfowler42, @lea_starita, @erezaterez, @JShendure, @YangKevinK, @deboramarks, ...

          1 reply 0 proslijeđenih tweetova 8 korisnika označava da im se sviđa
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        5. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Why? Proteins are complex and still mysterious in many ways. How can we smartly engineer when we can’t predict what happens when making even a single change to a protein sequence? High-throughput measurement technologies can fill the void in our understanding. But how to focus?

          1 reply 0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        6. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Following @paulg’s “make something people want”, I was searching for the perfect protein family: 1) state-of-the-art very suboptimal 2) many applications 3) people need it (enough to pay $$$), 4) hard to make it (otherwise why even develop a new technology?), 5) health relevant

          1 reply 0 proslijeđenih tweetova 7 korisnika označava da im se sviđa
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        7. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Discovered #AAV thanks to @ChewWeiLeong's early work in @geochurch lab delivering #CRISPR #Cas9 with #AAV. It was a perfect fit, an elegant and minimal genetic system, plus bonus: built in genotype-phenotype coupling (capsids package their own genomes!)pic.twitter.com/lEeC1m4Bz4

          1 reply 0 proslijeđenih tweetova 6 korisnika označava da im se sviđa
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        8. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          More than just being interesting, I wanted to be sure there really were real world applications. Thanks to ElizabethLeshen, WhitneySnider, SarahHonig & ElenaViboch who helped me understand the business of #AAV #GeneTherapy w/ @VickiSato in @HarvardHBS CommercializingScience

          1 reply 0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        9. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          + thanks to many who volunteered their time and opinions to set us on the right path! ShenShen, AriFriedland, @ksbosley, @mannysimons, JamesMcLaughlin, @PKolchinsky, BenAuspitz, FeliciaPagliuca, ...

          0 proslijeđenih tweetova 3 korisnika označavaju da im se sviđa
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        10. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          The foundations of #GeneTherapy today are built on natural #AAV capsids, but these aren’t optimal in many ways: we want them to target new cell types more efficiently and specifically. Plus avoid the immune system, package more DNA, and be cheaper to produce.

          1 reply 0 proslijeđenih tweetova 7 korisnika označava da im se sviđa
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        11. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          #AAV capsids are complex! 60 monomers assemble around the DNA genome, then capsids must navigate within the body to cellular targets, enter cells within an endosome, break free of those endosomes, get into the nucleus and finally release their payloads...

          1 reply 0 proslijeđenih tweetova 6 korisnika označava da im se sviđa
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        12. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          This multiplicity of critical functions makes #AAV challenging to engineer. If you improve only a single function, but the other functions get worse, that’s no good. And this happens often, because most changes to the protein are deleterious!

          1 reply 0 proslijeđenih tweetova 7 korisnika označava da im se sviđa
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        13. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Standard protein engineering approaches like rational design and random mutagenesis favor libraries of capsids of either high quality or of high quantity, but struggle to achieve both simultaneously. We thought we could add something unique to the field...

          1 reply 0 proslijeđenih tweetova 4 korisnika označavaju da im se sviđa
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        14. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          It’s an amazing time to be in #GeneTherapy. We benefited hugely from pioneering works by HiroyukiNakai, LukVandenberghe, AravindAsokan, TomasBjorklund, JimWilson, DirkGrimm, SergeiZolotukhin, @Nicole_Paulk, ThomasWeber, @Thieum001, @bendeverman, + many discussions w/ same

          0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        15. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Inspired by work from my PhD with @hattaca, @jimmyx66, NivCohen, @harriswangnyc and @RoyKishony, we knew that a comprehensive scanning library of all possible single mutations would be blueprint for understanding how to engineer the #AAV capsid https://www.ncbi.nlm.nih.gov/pubmed/28009265 

          1 reply 0 proslijeđenih tweetova 6 korisnika označava da im se sviđa
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        16. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Just substitute, insert or delete every possible amino acid one by one at each position across the capsid protein, about 30k variants. Simple idea. Two years later, and...success!pic.twitter.com/cZP5h05sGV

          1 reply 0 proslijeđenih tweetova 9 korisnika označava da im se sviđa
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        17. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Actually, it wasn’t that easy. Biggest challenges occurred during many failed attempts to link mutations and barcodes (beware template switching). We cloned the library 3 different ways before settling on a synthesis-and-split approach that beautifully solved the problem.pic.twitter.com/ybZQBX9U2v

          1 proslijeđeni tweet 15 korisnika označava da im se sviđa
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        18. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Directly synthesizing the library, we were able to make 99.9% of all single amino acid mutants. Once we had the library assembled, we could measure the function of all variants in a single experiment, even in a single animal! Our goal was to get PoC measurements in mice.

          0 proslijeđenih tweetova 2 korisnika označavaju da im se sviđa
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        19. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          But first came a BIG surprise. Rather than making only amino acid changes, we incorporated all codon changes, thinking these would reveal non-coding effects. Here you can see how mutations in synonymous codons indicate the presence of something odd, definitely a pattern here...pic.twitter.com/mB4ouucH9e

          1 reply 3 proslijeđena tweeta 9 korisnika označava da im se sviđa
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        20. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Long story short: we found a new gene in the early VP1 region of the capsid, which had gone unnoticed during more than 50 years of studying #AAV. One of my most exciting surprises of my career!

          1 reply 1 proslijeđeni tweet 16 korisnika označava da im se sviđa
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        21. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          The new protein associates with the membrane, so we called it membrane-associated accessory protein (MAAP). Many questions remain, but it appears within all the other popular AAV serotypes we tested (2/5/8/9). #AAV now with #MAAP

          1 reply 1 proslijeđeni tweet 2 korisnika označavaju da im se sviđa
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        22. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Back to our main story, our goal was to use the library to understand how #AAV works in vivo. Could we learn anything about why capsids go toward particular organs within the body?pic.twitter.com/vPnQsibTXK

          1 reply 0 proslijeđenih tweetova 3 korisnika označavaju da im se sviđa
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        23. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          We injected the virus library into mice to measure biodistribution: most mutants clustered into a few distinct profiles. In other words, it should be possible to build predictive models from a relatively small number of measurements! A promising sign for #MachineGuideDesign.pic.twitter.com/9bBCTCUvSU

          1 reply 0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        24. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Together with @samsinai, we put this idea to the test. We designed new multi-mutants sequences (with many changes) using data from our single-mutant dataset. Would this data enable us to build better libraries than a random approach?pic.twitter.com/xYLH0bB1S3

          1 reply 0 proslijeđenih tweetova 4 korisnika označavaju da im se sviđa
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        25. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          As we had hoped, our #MachineGuidedDesign strategy was much better at generating viable mutants than were randomly generated sequences. This demonstrates that such data can accelerate the search for synthetic #AAV capsids that have improved properties for #GeneTherapy.pic.twitter.com/XFculK8HCx

          1 reply 0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        26. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Libraries like these will make great training data for machine learning algorithms, for even more informed engineering. Stay tuned for exciting future updates. #DeepSequenceSpace meets #DeepLearning.

          1 reply 0 proslijeđenih tweetova 3 korisnika označavaju da im se sviđa
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        27. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Almost a year ago, I left @harvardmed to focus on translating these technologies into making optimized #AAV for human therapy. Joined by amazing Co-founders including @samsinai, @SSlomovic, @Adrian_Veres_, @geochurch and now many more at @dyno_tx, http://www.dynotx.com pic.twitter.com/4e6OI7xcMh

          1 reply 0 proslijeđenih tweetova 6 korisnika označava da im se sviđa
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        28. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Many efforts underway to improve these technologies and scale up our high-throughput experimental + data pipelines and #MachineLearning models. We are rapidly growing and actively hiring! Check out open positions http://dynotx.com/#careers pic.twitter.com/nUkMNFmzMb

          1 reply 0 proslijeđenih tweetova 5 korisnika označava da im se sviđa
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        29. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Thanks to the many folks in @geochurch lab, @wyssinstitute and beyond who helped make this possible! @eskamoah, @SurgeBiswas, @glebkuz, NinaJain, @dbgoodman, MaxSchubert, DavidThompson, IsaacHan, DenitsaMilanova, JohnAach, JayCulverwell, MaryTolikas, AyisAntoniou!

          1 reply 0 proslijeđenih tweetova 6 korisnika označava da im se sviđa
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        30. Eric Kelsic‏ @ekelsic 29. stu 2019.
          • Prijavi Tweet

          Anyone interested to learn more check out this review I wrote with @geochurch on #MachineGuidedDesign for #AAV! https://insights.bio/cell-and-gene-therapy-insights/journal/articles/challenges-and-opportunities-of-machine-guided-capsid-engineering-for-gene-therapy/ …

          0 replies 2 proslijeđena tweeta 15 korisnika označava da im se sviđa
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