Muhammad Munim
@mbmunim_
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PhD candidate - @mvh_lab @MIT - fascinated by all things metabolism | @UofT '20 | 🇵🇰🇨🇦 he/him
Joined September 2018
Thrilled to share our paper out today. I am immensely grateful to my mentor, Dr. Ömer H. Yilmaz @ohyilmaz , and our fantastic collaborators @JudithAgudo1 & labmates and @DamonRunyon for five years of support.
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the bureaucrats buying bunsen burners fear the Lab Candle Enjoyer
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Great lab bonding at our 2025 lab retreat. A good mix of scientific discussion and lab fun. I'm so grateful for such an inspiring and energetic lab!
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being the first to jaywalk is so aura inducing the other pedestrians all look at each other like damn he’s really him
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Happy to announce that I was awarded a pathway to independence award (K99) from the NCI/NIH today to work on studying whole-body metabolism driving cancer cachexia. None of this would be possible without support from @DamonRunyon and mentorship from @Transactivator and mvhlab.
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Uh oh: deep learning models in genomics lose badly to very simple linear models. Could they have been over-hyped? (new paper by @s_anders_m @const_ae and Wolfgang Huber)
nature.com
Nature Methods - The analysis presented in this Brief Communication shows that, despite their complexity, current deep learning models do not outperform linear baselines in predicting gene...
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calenderheads peep february 2026. you dont see em laid out like this often anymore. something to look forward to
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‼️Excited to share my first preprint in the Stegmaier lab – using metabolite depletion screens in physiological culture medium and in vitro/in vivo CRISPR screens, we identify metabolism of vitamin B2 (riboflavin) as a potent dependency in acute myeloid leukemia (AML). A 🧵…
Riboflavin drives nucleotide biosynthesis and iron-sulfur metabolism to promote acute myeloid leukemia https://t.co/Q7J3blQR6X
#biorxiv_cancer
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If I'm reading the new STATE ( Adduri et al. , https://t.co/8MfbP1ATJb ) paper correctly, we still cannot predict the effect of genetic perturbations better than a simple mean, and many methods do worse?
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AHH!! My primary thesis work is finally out! Tweetorial to come...
New online! ACAD10 and ACAD11 enable mammalian 4-hydroxy acid lipid catabolism https://t.co/JxDn3pT7lW
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im not a big gossip but i love Knowing Things. so when i ask for gossip it's never sinister. it's pure curiosity and a desire for knowledge
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Recent Beamish award winner @TaoThuan @NatMetabolism @UBC describes how to avoid “nonsensical” results in metabolomics https://t.co/mtOMxHb74p
nature.com
Nature Metabolism - Pathway analysis, originally developed for gene expression data, has been adapted for metabolomics. However, owing to the unique characteristics and constraints of metabolites...
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A case for substituting butter with plant oils From >220,000 participants in 3 large cohorts using validated food questionnaires with 33 years of follow-up https://t.co/1ReOBeobXn
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Honoured to have been invited to this symposium! Had a great time presenting my work and interacting with other amazing students!
CSHL hosted its first Innovators Symposium! Led by postdocs, the meeting featured senior grad students the opportunity to present their work and connect with faculty and researchers to help further their scientific careers.
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will forever point out that PhyloP, a traditional probabilistic model, has a fraction of the compute cost and does as well or better than many of these foundation models
New from @arcinstitute: Evo 2, the largest (by training compute) biology ML model ever, and one of the largest-ever open source ML models in any category. Evo 2 is a foundation model trained on 9T DNA base pairs that learns a lot of fundamental details about life. A few examples
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Seasonal depression “isn’t real” until you get a warm day in February and realize you’ve been dead inside for months
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I can’t believe it - after years of advocacy, exclusionary zoning has ended in Cambridge. We just passed the single most comprehensive rezoning in the US—legalizing multifamily housing up to 6 stories citywide in a Paris style Here’s the details 🧵
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New preprint claims that most existing DNA language models perform just as well with random weights, suggesting that pretraining does nothing (Mistral & DNABERT-2 look like exceptions). We need better DNA language models.
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and I don't just mean papers - I mean reagents, code, IP - everything
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