About

Sounak Sahu, PhD, is an Assistant Professor in the Department of Cell Biology at NYU Grossman School of Medicine. His laboratory focuses on advancing understanding of human development, disease, and precision medicine through innovative approaches in stem cell biology and functional genomics. His research themes include the use of embryonic stem cells and organoids to study development and regenerative medicine, investigating how developmental processes are exploited in cancer progression, particularly in breast cancer, and applying high-throughput functional genomics to interpret DNA variants relevant to cancer diagnosis and therapy. Dr. Sahu's work involves generating three-dimensional organoid models that recreate aspects of early human development, enabling detailed study of tissue differentiation and morphogenesis. He explores the parallels between embryonic development and cancer, aiming to identify regulatory circuits involved in both processes and uncover potential therapeutic targets. His research also integrates CRISPR-based tools with stem cell and organoid models to systematically assess the impact of genetic variants, contributing to the field of precision medicine. His contributions are shaping the future of developmental biology, regenerative medicine, and cancer therapeutics.

Research topics

• Chemistry
• Computer Science
• Biophysics
• Genetics
• Chemical physics
• Materials science
• Nanotechnology
• Biology
• Cell biology

Selected publications

• BPS2026 – Spatio-temporal dynamics of active and passive nucleo-cytoplasmic transport in live human cells

  Biophysical Journal · 2026-02-01

  article1st authorCorresponding
  PublisherDOI

• MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

  ArXiv.org · 2026-04-15

  articleOpen access

  We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal systems -- Benzamide, Benzoic acid, Coumarin, Durene, Isonicotinamide, Niacinamide, Nicotinamide, Pyrazinamide, and Resorcinol -- developed using the Automated Machine Learning Pipeline (AMLP), which streamlines the entire MLIP development workflow, from reference data generation to model training and validation, into a reproducible and user-friendly pipeline. Models are fine-tuned from the MACE-MH-1 foundation model (omol head), yielding a mean energy MAE of 0.141 kJ/mol/atom and a mean force MAE of 0.648 kJ/mol/Angstrom across all systems. Dynamical stability and structural integrity, as assessed through energy conservation, P2 orientational order parameters, and radial distribution functions, are evaluated using molecular dynamics simulations. The released models and datasets constitute a growing open database of validated MLIPs, ready for production MD simulations of molecular crystal polymorphism under different thermodynamic conditions.

  PublisherOA PDF

• MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

  arXiv (Cornell University) · 2026-04-15

  preprintOpen access

  We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal systems -- Benzamide, Benzoic acid, Coumarin, Durene, Isonicotinamide, Niacinamide, Nicotinamide, Pyrazinamide, and Resorcinol -- developed using the Automated Machine Learning Pipeline (AMLP), which streamlines the entire MLIP development workflow, from reference data generation to model training and validation, into a reproducible and user-friendly pipeline. Models are fine-tuned from the MACE-MH-1 foundation model (omol head), yielding a mean energy MAE of 0.141 kJ/mol/atom and a mean force MAE of 0.648 kJ/mol/Angstrom across all systems. Dynamical stability and structural integrity, as assessed through energy conservation, P2 orientational order parameters, and radial distribution functions, are evaluated using molecular dynamics simulations. The released models and datasets constitute a growing open database of validated MLIPs, ready for production MD simulations of molecular crystal polymorphism under different thermodynamic conditions.

  PublisherDOI

• BPS2025 - Spatio-temporal dynamics of active and passive nucleo-cytoplasmic transport in live human cells

  Biophysical Journal · 2025-02-01

  article1st authorCorresponding
  PublisherDOI

• Ionic strength alters crosslinker‐driven self‐organization of microtubules

  Cytoskeleton · 2024-02-22 · 4 citations

  articleOpen access

  The microtubule cytoskeleton is a major structural element inside cells that directs self-organization using microtubule-associated proteins and motors. It has been shown that finite-sized, spindle-like microtubule organizations, called "tactoids," can form in vitro spontaneously from mixtures of tubulin and the antiparallel crosslinker, MAP65, from the MAP65/PRC1/Ase family. Here, we probe the ability of MAP65 to form tactoids as a function of the ionic strength of the buffer to attempt to break the electrostatic interactions binding MAP65 to microtubules and inter-MAP65 binding. We observe that, with increasing monovalent salts, the organizations change from finite tactoids to unbounded length bundles, yet the MAP65 binding and crosslinking appear to stay intact. We further explore the effects of ionic strength on the dissociation constant of MAP65 using both microtubule pelleting and single-molecule binding assays. We find that salt can reduce the binding, yet salt never negates it. Instead, we believe that the salt is affecting the ability of the MAP65 to form phase-separated droplets, which cause the nucleation and growth of tactoids, as recently demonstrated.

  PublisherOA PDFDOI

• Protein condensate fusion mapping using Hippopede curve

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-10-17 · 1 citations

  preprintOpen access1st authorCorresponding

  Protein and nucleic acid condensation in cells has been a major focus of biophysical studies in recent years. Such droplets, condensates, or membraneless cellular organelles that form by condensation have been shown to behave like liquid or viscoelastic fluids. In vitro reconstitution of condensates of purified protein and nucleic acid have been used to better understand the phenomena, specifically to quantify the material properties of these condensates. One of the signature properties of a liquid-like condensate is the fusion of two round droplets into a larger droplet. Quantitative imaging and analysis of the fusion process can report the biophysical properties of the condensates, such as surface tension and viscosity in a non-invasive way. Here we take advantage of a model in soft matter physics, hippopede curves, to the condensate fusion problem to estimate these biophysical properties directly and compare the results with existing models in the field. In addition, this method approximates additional parameters such as the surface area and surface curvatures during the fusion process over time.

  PublisherOA PDFDOI

• Interplay of self-organization of microtubule asters and crosslinking protein condensates

  PNAS Nexus · 2023 · 25 citations

  1st authorCorresponding
  • Biology
  • Cell biology
  • Biophysics

  The cytoskeleton is a major focus of physical studies to understand organization inside cells given its primary role in cell motility, cell division, and cell mechanics. Recently, protein condensation has been shown to be another major intracellular organizational strategy. Here, we report that the microtubule crosslinking proteins, MAP65-1 and PRC1, can form phase separated condensates at physiological salt and temperature without additional crowding agents in vitro. The size of the droplets depends on the concentration of protein. MAP65 condensates are liquid at first and can gelate over time. We show that these condensates can nucleate and grow microtubule bundles that form asters, regardless of the viscoelasticity of the condensate. The droplet size directly controls the number of projections in the microtubule asters, demonstrating that the MAP65 concentration can control the organization of microtubules. When gel-like droplets nucleate and grow asters from a shell of tubulin at the surface, the microtubules are able to re-fluidize the MAP65 condensate, returning the MAP65 molecules to solution. This work implies that there is an interplay between condensate formation from microtubule-associated proteins, microtubule organization, and condensate dissolution that could be important for the dynamics of intracellular organization.

  PublisherOA PDFDOI

• A dual role for the chromatin reader ORCA/LRWD1 in targeting the origin recognition complex to chromatin

  The EMBO Journal · 2023-08-08 · 14 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Interplay of self-organization of microtubule asters and crosslinking protein condensates Data

  Zenodo (CERN European Organization for Nuclear Research) · 2023-07-09

  datasetOpen access

  Data sets from all figures and supplemental figures for manuscript entitled "Interplay of self-organization of microtubule asters and crosslinking protein condensates" accepted at PNAS Nexus.

  PublisherDOI

• Interplay of self-organization of microtubule asters and crosslinking protein condensates Data

  Zenodo (CERN European Organization for Nuclear Research) · 2023-07-09

  datasetOpen access

  Data sets from all figures and supplemental figures for manuscript entitled "Interplay of self-organization of microtubule asters and crosslinking protein condensates" accepted at PNAS Nexus.

  PublisherDOI

Frequent coauthors

• Jennifer L. Ross

  Syracuse University

  22 shared

• Prashali Chauhan

  Syracuse University

  11 shared

• Bianca Edozie

  7 shared

• Hong Beom Lee

  Syracuse University

  6 shared

• Aaron J. Wolfe

  Clarkson University

  6 shared

• Ruell Branch

  Syracuse University

  6 shared

• Niaz Goodbee

  Syracuse University

  6 shared

• J. M. Schwarz

  Syracuse University

  6 shared

Labs

• Sahu LabPI