4 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…
Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data
Victor Chardès
Single-cell RNA-seq provides detailed molecular snapshots of individual cells but is notoriously noisy. Variability stems from biological differences and technical factors, such as…
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…
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…