Sevi Durdu

little living creatures

[Sevi Durdu @biosevi]

@AlejoFraticelli Thank you, Alejo! I’m so glad to hear our work can contribute to the scientific community. The question was indeed a tough nut to crack, but it seems TFs now will need some eccentric features to keep their secrets from us!

[Sevi Durdu @biosevi]

🚀Landmark paper out!
#Chromatin context + motif grammar explain how NGN2 vs MyoD1 guide neurons🧠 vs muscle💪 from similar motifs
#MachineLearning + assays uncover generalizable cooperative vs pioneer #TranscriptionFactor binding modes
📄 https://t.co/88Nwbpa3EP

[Schübeler Lab @SchubelerLab]

Excited to see this published with additional data following our preprint a while back. Cool combination (in our biased view) of controlled TF expression and machine learning to decode chromatin sensitivity. https://t.co/12VxDjpwsP.

[Sevi Durdu @biosevi]

🧬Preprint alert!
FGF3 isn’t just a paracrine signal, it’s a self-editing morphogen.
Fgf3-producing cells ignore parts of their own signal via nuclear repression of select target genes.
A built-in asymmetry between senders and receivers! #FGF #Morphogen

https://t.co/4mk089GJfM

[Sevi Durdu @biosevi]

@joadelas It was a pleasure to discuss with you!

[Schübeler Lab @SchubelerLab]

MyoD1 makes myocytes and NGN2 neurons so how is specificity achived when motifs occur millions of times? Using controlled TF induction in murine stem cells we profile genome accessibility, chromatin modifications and TF binding before and following activation 2/7

[Schübeler Lab @SchubelerLab]

Excited to share our latest preprint. Lead by Sevi (@biosevi) we asked how transcription factors (TFs) with short and abundant motifs drive specific cell fates. Our test cases: Two bHLH factors that bind to seemingly similar E-boxes. 1/7 https://t.co/WPNx5LrIUJ

[Sevi Durdu @biosevi]

@AndrewWoodLab famous notch signalling ligand DLL https://t.co/JOHXxozPmJ