A. Zaritsky

Cell Width in Escherichia coli: Interrogating Coupling to Nucleoid Structure

The paper investigates how nucleoid complexity influences cell width in Escherichia coli by analyzing the relationship between replication and growth rates.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

The paper uses machine learning to track continuous state transitions in myoblast differentiation and fusion, revealing timing and regulatory roles of ERK1/2 and p38.

Emergence of synchronized multicellular mechanosensing from spatiotemporal integration of heterogeneous single-cell information transfer

The paper quantifies how individual cell behaviors contribute to multicellular synchronization through information transfer in endothelial cell monolayers.

Inference of long-range cell-cell force transmission from ECM remodeling fluctuations

This paper introduces a computational method to infer long-range cell-cell force transmission through the extracellular matrix using ECM remodeling fluctuations, demonstrating its effectiveness in fibroblast and cancer cell models.

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

This paper uses an adversarial auto-encoding deep learning model to classify metastatic efficiency in melanoma cells from label-free live cell images and identifies pseudopodial extensions and light scattering as functional hallmarks.

Recombination between co-infecting herpesviruses occurs where replication compartments coalesce

This study shows that herpesvirus recombination occurs when replication compartments coalesce, revealing spatial and temporal requirements for inter-genomic recombination.

Formation and disassembly of a contractile actomyosin network mediates content release from large secretory vesicles

The paper identifies an actin assembly and disassembly cycle in Drosophila salivary gland vesicles that drives secretion by coordinating Rho1 signaling and actin dynamics.

Decoupling global biases and local interactions between cell biological variables

The paper introduces DeBias, a method to separate global biases from local interactions in cell biology data, showing global bias contains mechanistic insights.