Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2107.14751

Actin reorganization throughout the cell cycle mediated by motor proteins

Maria-Veronica Ciocanel·Aravind Chandrasekaran·Carli Mager·Qin Ni·Garegin Papoian·Adriana Dawes
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cortical actin networks are highly dynamic and play critical roles in shaping the mechanical properties of cells. The actin cytoskeleton undergoes significant reorganization over the course of the cell cycle, when cortical actin transitions between open patched meshworks, homogeneous distributions, and aligned bundles. Several types of myosin motor proteins, characterized by different kinetic parameters, have been involved in this reorganization of actin filaments. Given the limitations in studying the interactions of actin with myosin in vivo, we propose stochastic agent-based model simulations and develop a set of data analysis measures to assess how myosin motor proteins mediate various actin organizations. In particular, we identify individual motor parameters, such as motor binding rate and step size, that generate actin networks with different levels of contractility and different patterns of myosin motor localization. In simulations where two motor populations with distinct kinetic parameters interact with the same actin network, we find that motors may act in a complementary way, by tuning the actin network organization, or in an antagonistic way, where one motor emerges as dominant. This modeling and data analysis framework also uncovers parameter regimes where spatial segregation between motor populations is achieved. By allowing for changes in kinetic rates during the actin-myosin dynamic simulations, our work suggests that certain actin-myosin organizations may require additional regulation beyond mediation by motor proteins in order to reconfigure the cytoskeleton network on experimentally-observed timescales.

Explore related subjects

q-bio.QMq-bio.SC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maria-Veronica Ciocanel, Aravind Chandrasekaran, Carli Mager, Qin Ni, Garegin Papoian, Adriana Dawes. 2021-07-30. Actin reorganization throughout the cell cycle mediated by motor proteins. https://doi.org/10.1371/journal.pcbi.1010026

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

Predicting clinical outcomes from preclinical data is essential for selecting safe and effective drug combinations and for reducing late-stage failures. AI models use molecular structure and target annotations, and do not leverage the perturbation readouts that report how a compound acts in a cellular context. Here we introduce Madrigal, a multimodal AI model that learns from structural, pathway, cell-viability, and transcriptomic data. Madrigal aligns these modalities across 21,842 compounds into a shared latent space and predicts combination outcomes even for drugs observed in only a subset of the data modalities. Trained on 158 expert-curated and 795 patient-reported combination outcomes, Madrigal outperforms single-modality and state-of-the-art multimodal methods. Ablations show that modality alignment and multimodal input each improve predictive performance. Madrigal predicts elevated risk for combinations that share membrane transporters. In head-to-head trials that compare two combination arms,the arm with the higher observed incidence of neutropenia, anemia, alopecia, or hypoglycemia receives the higher predicted risk in 25 of 28 comparisons. In MASH, Madrigal ranks resmetirom among the candidates with favorable predicted safety when paired with type 2 diabetes drugs. Madrigal also improves adverse-event prediction in a longitudinal patient cohort and an independent oncology cohort and predicts efficacy in primary acute myeloid leukemia samples and patient-derived xenografts.

q-bio.QM↗
arXiv2026

ProteoEM: probabilistic protein abundance estimation from iterative affinity traces

Single-molecule affinity mapping enables molecular-level measurement of proteins and proteoforms, but imperfect and nonspecific probe binding makes individual affinity traces compatible with multiple molecular identities. Accurate abundance estimation therefore requires apportionment of ambiguous traces by weight rather than assignment to a single candidate. We developed ProteoEM, an expectation-maximization framework for weighted proteoform quantification, inspired by transcript abundance estimation methods for RNA sequencing and released as an open-source Python package. ProteoEM evaluates each molecule against every candidate using fixed, pre-calibrated probe-response rates held separate from the abundance estimate, while retaining the full likelihood of the observed affinity features. The framework estimates proteoform abundances, reports indistinguishable proteoforms as groups when measurements cannot separate them, and accounts for differential observation yields to distinguish the composition of observed molecules from that of the source sample. In simulations, ProteoEM accurately recovered the underlying molecular composition where approaches that reduce each trace to a hard yes/no call introduced substantial errors. ProteoEM's performance was insensitive to a moderate, uniform calibration error but was biased by informative missing data and by proteoforms absent from the reference. When observation yields were known, it also recovered source-sample composition from observed molecular counts. ProteoEM provides an open-source, reproducible framework for quantitative analysis of single-molecule affinity measurements, and these results motivate validation on experimental molecule-level data.

q-bio.QM↗
arXiv2026

PRAXIS-VirtualCell: A Programmable and Trustworthy Framework for Agentic Virtual Cell Experiments

Virtual cells are evolving from single-task predictive models toward programmable biological simulation systems, yet heterogeneous data, models, and validation evidence still lack a unified organizational framework. Here, we present PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments. The system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, while using biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention. By further integrating agentic orchestration, PRAXIS-VirtualCell automatically translates natural-language questions into traceable virtual experiments, providing a unified runtime foundation for trustworthy and scalable Virtual Cell systems.

q-bio.QM↗