26 citations · 53 across the 11 of their papers we have counts for
13 papers
Learning biophysical models of gene regulation with probability flow matching
Suryanarayana Maddu, Victor Chardès, Michael J. Shelley
Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellu…
Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in Human Mitotic Spindles
Colm P. Kelleher, Suryanarayana Maddu, Mustafa Basaran +3
How thousands of microtubules and molecular motors self-organize into spindles remains poorly understood. By combining static, nanometer-resolution, large-scale electron tomography…
Inferring stochastic dynamics with growth from cross-sectional data
Stephen Zhang, Suryanarayana Maddu, Xiaojie Qiu +1
Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
Inferring biological processes with intrinsic noise from cross-sectional data
Suryanarayana Maddu, Victor Chardès, Michael. J. Shelley
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explo…
Stochastic force inference via density estimation
Victor Chardès, Suryanarayana Maddu, Michael J. Shelley
Inferring dynamical models from low-resolution temporal data continues to be a significant challenge in biophysics, especially within transcriptomics, where separating molecular pr…