Suryanarayana Maddu
@surimk92
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Flatiron research fellow @FlatironInst @SimonsFdn @Harvard QBio. Works on problems at the interface of AI + computing + biology.
Joined April 2014
great resources for the biophysics community!!.
🧬🔬🧪 🎉 Our team at @czbiohub is thrilled to share TWO companion papers out today in @NatureMethods!.📦 Ultrack — robust, scalable nD cell tracking.🌐 inTRACKtive — a beautiful, open-source web viewer for lineage exploration.Let’s dive in! 👇 (LINKS BELOW)
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This is probably one of the coolest applications of GenAI. remarkable !!.
At @OpenAI, we believe that AI can accelerate science and drug discovery. An exciting example is our work with @RetroBiosciences, where a custom model designed improved variants of the Nobel-prize winning Yamanaka proteins. Today we published a closer look at the breakthrough. ⬇️
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Need of the hour !!.
Our new Simons Collaboration on the Physics of Learning and Neural Computation will employ and develop powerful tools from #physics, #math, computer science and theoretical #neuroscience to understand how large neural networks learn, compute, scale, reason and imagine:.
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RT @colmpkelleher: Excited to share our new paper: "Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in….
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4/4 Despite different imaging principles and spatial resolution, fluctuation spectra from ET and light microscopy agree remarkably well, validating the model across modalities. In summary, even in complex cellular organelles like the spindle, active-matter physics combined with.
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3/4 Our model reveals that spindle organization is governed by: local MT alignment interaction, diffusive-like motion, directed transport & MT turnover dynamics, all encoded in our continuum theory with interpretable parameters. These measured parameters provide a quantitative.
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2/4 We combine detailed static ultrastructural data from electron tomography with dynamic polarization microscopy 🔬 measurements to find out. We demonstrate that a simple coarse-grained active liquid crystal model can quantitatively explain both the mean structure and.
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🧵1/4 Excited to share our new paper:. "Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in Human Mitotic Spindles". Joint work with @colmpkelleher @FlatironInst . How do thousands of microtubules and associated proteins
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RT @MOSAICgroup1: ⚡️Advanced Computing ❤️ laboratory experiments ➡️ an unbeatable combination to unlock the secrets of biology! Together wi….
journals.biologists.com
Highlighted Article: Image-based 3D modeling of pore-scale extracellular space geometries of the developing zebrafish embryo reveals that the Fgf8a morphogen gradient is regulated by the porous media...
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🚀Excellent follow-up by Stephen extending Probability Flow Inference to account for cell proliferation. SOTA performance on time-resolved scRNA-seq to infer transcriptional flow dynamics. In short: inverting the Fokker-Planck in 10–20D with cell growth @
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RT @YoungBiophyMeet: Mark your calendars!! 🗓️ The third Young Biophysicists Meeting will be held on the coming Monday (May 5th) from 11am….
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RT @HenryHMattingly: Finally out! An analytical method for deriving diffusion coefficients of bacteria in disordered media, by compressing….
pnas.org
Bacterial motility in spatially structured environments impacts a variety of natural and engineering processes. Constructing models to predict, con...
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Inferring local dominant mechanisms in active matter systems !!!.
Learning interpretable mathematical models from data is transforming science. But what if different models apply in different areas or at differnt times? After all, living systems are plastic and self-organized. Now you can learn *local* model clusters!
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RT @MilesCranmer: 🧵 Could this be the ImageNet moment for scientific AI?. Today with @PolymathicAI and others we're releasing two massive….
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Introducing The Well: 15TB of standardized scientific simulation datasets compiled from various domains — a big step forward for the sciML community. Glad to be a small part of this big effort. #NeurIPS2024.
Generating cat videos is nice, but what if you could tackle real scientific problems with the same methods? 🧪🌌.Introducing The Well: 16 datasets (15TB) for Machine Learning, from astrophysics to fluid dynamics and biology. 🐙: 📜:
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RT @SimonsFdn: “It’s important to figure out how the process is naturally regulated, as well as how to control it.” – @FlatironInst biologi….
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