Search arXiv⌕ Search

arXiv · q-bio/0311032

Persistence length of chromatin determines origin spacing in Xenopus early-embryo DNA replication: Quantitative comparisons between theory and experiment

Abstract

In Xenopus early embryos, replication origins neither require specific DNA sequences nor is there an efficient S/M checkpoint, even though the whole genome (3 billion bases) is completely duplicated within 10-20 minutes. This leads to the"random-completion problem" of DNA replication in embryos, where one needs to find a mechanism that ensures complete, faithful, timely reproduction of the genome without any sequence dependence of replication origins. We analyze recent DNA replication data in Xenopus laevis egg extracts and find discrepancies with models where replication origins are distributed independently of chromatin structure. Motivated by these discrepancies, we have investigated the role that chromatin looping may play in DNA replication. We find that the loop-size distribution predicted from a wormlike-chain model of chromatin can account for the spatial distribution of replication origins in this system quantitatively. Together with earlier findings of increasing frequency of origin firings, our results can explain the random-completion problem. The agreement between experimental data (molecular combing) and theoretical predictions suggests that the intrinsic stiffness of chromatin loops plays a fundamental biological role in DNA replication in early-embryo Xenopus in regulating the origin spacing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Suckjoon Jun, John Herrick, Aaron Bensimon, John Bechhoefer. 2003-11-24. Persistence length of chromatin determines origin spacing in Xenopus early-embryo DNA replication: Quantitative comparisons between theory and experiment. https://doi.org/10.4161/cc.3.2.655

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

KEEP EXPLORING

Related papers

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↗

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↗

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↗