Biology subjects

Sheinman, M.

Publications and source records attributed to Sheinman, M..

9 recordsLinked to original sources

bioRxiv2026

Using Bayesian inference to characterize cell-size regulation across organisms

How organisms regulate cell size and whether this varies with growth conditions remains a fundamental question. Inferring how cells regulate their size requires separating biological control from statistical effects. Yet regulatory strategies are commonly classified by estimating the size-control parameter, , from single-cell measurements using linear regression. We show that this conventional estimator is systematically biased by measurement noise, lineage-to-lineage extrinsic variability, and finite lineage length. Consequently, the same underlying regulatory mechanism can be misclassified as sizer-, adder-, or timer-like. We develop a Bayesian framework that jointly infers and the amplitudes of intrinsic, extrinsic, and measurement noise while accounting for lineage heterogeneity and finite sampling. Across simulations spanning experimentally relevant noise levels and lineage lengths, the framework recovers and outperforms conventional regression, particularly for short and heterogeneous lineages. Reanalysis of diverse bacterial and eukaryotic datasets collected under multiple growth conditions indicates that statistical bias accounts for much of the reported variation in across organisms and growth conditions. After accounting for these biases, the datasets are broadly consistent with adder-like regulation. Thus, our results provide a general framework for distinguishing genuine biological differences from noise-induced changes in inferred regulatory strategy and revise how cell-size regulation should be inferred from lineage data.

biophysics↗

bioRxiv2026

Inferring evolutionary distances from whole prokaryotic genome alignments, accounting for among-locus substitution rate heterogeneity

Ever since the rise of next generation sequencing technologies, the number of prokaryotic genomes in databases has increased tremendously. In order to leverage those vast quantities of data, an approach was previously introduced to estimate evolutionary divergences by modeling the genomic distances between substitutions in many whole genome alignments. However, this proof of concept study was conducted on the Enterobacterales clade only, such that its applicability to other clades remained to be established. In this study, we demonstrate the broad applicability of this method to a wider range of clades and to species with larger evolutionary divergences. We also show that relaxing a hypothesis on the substitution rate distribution, with a single additional free parameter, drastically improves both the estimation of evolutionary divergences and the quality of the inferred trees. We use simulations to show that our method can correctly recover the tree topology in cases where substitution rates evolve along the tree. Finally, our results also have theoretical implications for our understanding of substitution rate heterogeneity in genomes, since we found differences in the range of substitution rate distribution in our three examples Bacillales, Enterobacterales and Methanobacteriota whose causes remain to be identified.

evolutionary biology↗

bioRxiv2026

Disentangling mechanisms of single-cell growth rate fluctuations

Single-cell growth rates fluctuate across time and generations, but identifying the biological origin of this variability is difficult because growth rate is usually inferred from noisy measurements of cell size or mass rather than measured directly. Here, we develop a simple and interpretable framework that separates measurement noise from biological sources of growth rate variability. We show that the autocovariance of inferred instantaneous growth rates carries robust signatures of the measurement process that are largely independent of the underlying biological growth dynamics, allowing the form and magnitude of measurement noise to be identified directly from data before introducing a model for the biological dynamics. We then use the autocovariance of accumulated growth to distinguish continuous within-cycle fluctuations, division-associated perturbations, and lineage-to-lineage variability. Applying this framework to bacterial and mammalian single-cell datasets, we find evidence for continuous growth rate noise in both systems. In E. coli, division-associated perturbations are large at birth compared with continuous fluctuations, but their contribution to growth accumulated over the full cell cycle is reduced by rapid relaxation. In contrast, mammalian cells show no division kicks, but stronger lineage-to-lineage variability. More broadly, our results provide a direct and interpretable route to identifying the biological origin of growth rate variability in noisy single-cell measurements.

cell biology↗

bioRxiv2026

Cell Growth and Division Shape mRNA-Protein Correlations

1Correlations between cellular variables, such as gene-expression levels, provide insights into regulatory mechanisms. We focus here on correlations between mRNA and protein levels and re-examine previously derived analytical predictions. We test this prediction on single-cell E. coli data and see substantial disagreement. We hypothesize that this discrepancy arises from the assumption of constant cell volume and develop a theoretical framework for mRNA-protein correlations in growing and dividing cells. Within this framework, we derive an analytical expression for mRNA- protein correlations and show that explicit incorporation of growth and division substantially alters these correlations. The resulting relation is invariant to upstream transcriptional dynamics, and we validate it using stochastic simulations across multiple gene-regulatory architectures. Finally, we show that the derived predictions are consistent with the E. coli data.

biophysics↗

bioRxiv2025

Reconstructing the network of horizontal gene exchange in bacteria to differentiate direct and indirect transfers

Horizontal gene transfer (HGT) plays a central role in bacterial evolution. Yet, its large-scale dynamics and underlying network structure remain poorly characterized. We present a theoretical framework that models HGT as a continuous stochastic process over a network of bacterial genera and analyze its genomic footprint via the distribution of exact sequence matches shared across taxa--the match length distribution (MLD). We show that different evolutionary regimes imprint distinct statistical signatures on the MLD: single episodic gene transfer events yield exponential distributions, while continuous sustained HGT processes lead to power-law tails. The power-law exponent is analytically linked to the topology of the transfer network, distinguishing between intraclade transfers and hub-mediated dissemination. Empirical MLDs derived from bacterial genomes recapitulate these predicted patterns. Moreover, we find that defining a genus-specific "transferability" parameter that governs pairwise HGT rates and incorporating a high-transferability hub accurately reproduces the observed data. Our approach provides a general framework for inferring hidden structure in genomic horizontal transfer processes, enabling quantitative analysis of microbial evolution.

genomics↗

bioRxiv2023

An analytical derivation of the distribution of distances between heterozygous sites in diploid species to efficiently infer demographic history

Heterozygous sites are not uniformly distributed along a diploid genome. Rather, their density varies as a result of recombination events, and their local density reflects the time to the last common ancestor of the maternal and paternal copies of a genomic region. The distribution of the density of heterozygous sites therefore carries information about the history of the population size. Despite previous efforts, an exact derivation of the distribution of heterozygous sites is still lacking. As a consequence, the estimation of population size variation is difficult and requires several simplifying assumptions. Using a novel theoretical framework, we are able to derive an analytical formula for the distribution of distances between heterozygous sites. Our theory can account for arbitrary demographic histories, including bottlenecks. In the case of a constant population size the distribution follows a simple function and exhibits a power-law tail proportional to r with =-3, where r is the distance between heterozygous sites. This prediction is accurately validated when considering heterozygous sites in individuals of African descent. Other populations migrated out of Africa and underwent at least one bottleneck which left a distinct mark on their interval distribution between heterozygous sites, i.e., an overrepresentation of intervals between 10 and 100 kbp in length. Our analytical theory for non-constant population sizes reproduces this behavior and can be used to study historical changes in population size with high accuracy. The simplicity of our approach facilitates the analysis of demographic histories for diploid species, requiring only a single unphased genome.

genomics↗

bioRxiv2023

Modeling the mosaic structure of bacterial genomes to infer their evolutionary history

The timing and phylogeny of bacterial evolution is difficult to reconstruct because of a scarce fossil record, deep genomic divergences and complexities associated with molecular clocks. Studying bacterial evolutionary history using rich and rapidly accumulating genomic data requires accurate modeling of genome evolution, taking into account that different parts of bacterial genomes have different history. In particular, along the genome, different loci are subject to different selective pressure. In addition, some are horizontally transferred from one bacterium to another, resulting in a mosaic-like genome structure. An important technical aspect is that loci with high effective mutation rates can diverge beyond the aligner detection limit, biasing the genome-wide divergence estimate towards more conserved loci. Therefore, the genome-wide molecular clock cannot be directly applied to study bacterial evolutionary history. In this article, we propose a novel method to gain insight into bacterial evolution based on statistical properties of genomic sequences comparisons. The length distribution of the sequence matches is shaped by the effective mutation rates of different loci, by the horizontal transfers and by the aligner sensitivity. Based on these inputs we build a model and demonstrate that it accounts for the empirically observed distributions, taking the Enterobacteriaceae family as an example. Using the model and the empirical data we fit the evolutionary parameters: time divergences and horizontal transfer rates. Based on the estimated time divergences we build a time-calibrated phylogenetic tree, demonstrating the accuracy of the method and its ability to unravel vertical and horizontal transfers in bacterial genomes.

evolutionary biology↗

bioRxiv2020

Long identical sequences found in multiple bacterial genomes reveal frequent and widespread exchange of genetic material between distant species.

Horizontal transfer of genomic elements is an essential force that shapes microbial genome evolution. Horizontal Gene Transfer (HGT) occurs via various mechanisms and has been studied in detail for a variety of systems. However, a coarse-grained, global picture of HGT in the microbial world is still missing. One reason is the difficulty to process large amounts of genomic microbial data to find and characterise HGT events, especially for highly distant organisms. Here, we exploit the fact that HGT between distant species creates long identical DNA sequences in genomes of distant species, which can be found efficiently using alignment-free methods. We analysed over 90 000 bacterial genomes and thus identified over 100 000 events of HGT. We further developed a mathematical model to analyse the statistical properties of those long exact matches and thus estimate the transfer rate between any pair of taxa. Our results demonstrate that long-distance gene exchange (across phyla) is very frequent, as more than 8% of the bacterial genomes analysed have been involved in at least one such event. Finally, we confirm that the function of the transferred sequences strongly impact the transfer rate, as we observe a 3.5 order of magnitude variation between the most and the least transferred categories. Overall, we provide a unique view of horizontal transfer across the bacterial tree of life, illuminating a fundamental process driving bacterial evolution.

bioinformatics↗

bioRxiv2017

Effects of DNA oxidation on the evolution of genomes

Oxidation of DNA increases its mutation rate, causing otherwise rare G [->] T transversions during DNA replication. Here we use a comparative genomic approach to assess the importance of DNA oxidation for the evolution of genomic sequences. To do so, we study the mutational spectrum of Gn-tracks on various timescales, ranging from one human generation to the divergence between primates, and compare it to the properties of guanines oxidation known from experimental and computational studies. Our results suggest that, in short Gn tracks (n [≤] 3), oxidation does not dominate the mutagenesis of guanines, except in cancerous tumors, especially in lungs. However, we consistently find that the G [->] T transversion rate is elevated by an order of magnitude in long Gn tracks (n {gtrsim} 6). In such long Gn-tracks, G [->] T substitutions in fact dominate the mutational spectrum, suggesting that long Gn tracks are oxidized more frequently and/or repaired less efficiently.

evolutionary biology↗