Biology subjects

Ciarchi, M.

Publications and source records attributed to Ciarchi, M..

4 recordsLinked to original sources

bioRxiv2026

Dynamical Regimes in Rejuvenation

Biological aging is accompanied by systematic changes in epigenetic modifications and chromatin organization. The reversal of the effects of aging, rejuvenation, is experimentally achieved by the transient induction of factors that modify these marks in cells and organisms. Here, we show that key features of rejuvenation experiments emerge from the biophysical interplay between dynamic epigenetic marks and the three-dimensional conformation of chromatin. Using a minimal field theory and molecular dynamics simulations, we show that the system responds in three distinct temporal regimes. The intermediary regime fulfills necessary conditions for successful rejuvenation. In this regime, the system spends time near a separatrix, allowing for high epigenetic plasticity, while memory retained in the chromatin conformation enables restoration of the original epigenetic correlations. Analysis of sequencing data further supports the predicted coupling between chromatin compaction and epigenetic correlations. Our results provide a physical explanation for how rejuvenation may remodel age-associated epigenetic states without irreversibly erasing cellular identity. We identify a general mechanism by which memory stored in a slow structural variable permits reversible remodeling of a faster internal state.

biophysics↗

bioRxiv2025

A temporal hierarchy determines epigenetic aging

Aging involves processes spanning orders of magnitude in time, from fast events that occur at the molecular scale to the slow decrease of physiological function. Whether and how fast molecular events lead to the slow progression of aging, and what ultimately sets the timescale of aging, is not understood. Here, by focusing on dynamic changes in DNA methylation, we show how aging phenomena on long timescales emerge from the kinetics of fast molecular processes, providing a bridge between temporal scales. By combining DNA methylation sequencing data across a range of timescales with a statistical modeling-based approach, we show that DNA methylation aging is governed by a three-fold hierarchy of processes that dominate on distinct timescales: individual stochastic events in which enzymes interact with the DNA and with each other (milliseconds); the convergence of molecular concentrations to steady states (days to months); and stochastic transitions between these steady states (years to decades). Our findings provide a unified picture of how DNA methylation aging arises across temporal scales.

cell biology↗

bioRxiv2025

A transcriptional clock of the human pluripotency transition

Developmental timing differs strikingly between mammals. All embryonic and some placental lineages emerge from the pluripotent epiblast in a temporally defined sequence, lasting about two weeks in the human embryo, but only two days in mice. Moreover, the order of lineage segregation and gene expression differ between the species. We used human pluripotent stem cells to recapitulate this window of epiblast development in vitro. Simultaneous profiling of gene expression and chromatin accessibility in single cells revealed a robust, autonomous switch between cell states during this process. We reconstructed the integrative gene regulatory network (GRN) of transcription factors (TFs) and signalling molecules. Notably, this revealed a transcriptional cascade including temporally close positive and distant inhibitory connections. We suggest that this cascade acts as a transcriptional clock and governs the directionality, timing and intrinsic decisions during epiblast development. From individual gene interactions, we derived a mechanistic mathematical model of the transcriptional clock of pluripotency that closely reproduced gene expression dynamics and identified key regulatory connections. Moreover, our model revealed a bistable switch governing the transition, thus translating the GRN inference into an interpretable mechanism. Strikingly, the GRN model derived for humans predicted the acceleration of developmental timing in mice when initialised with mouse-specific expression patterns, yet still leading to a human-like expression state. Therefore, TF levels explain developmental timing, whereas the architecture of the network defines its trajectory. Together, our work provides novel conceptual insights into the intrinsic mechanisms of developmental transitions in the human embryo. HIGHLIGHTSO_LIThe human pluripotency transition is guided by an intrinsic developmental programme C_LIO_LIA cascade of transcription factors defines directionality and timing of the pluripotency transition through positive and negative interactions C_LIO_LITranscription factor levels explain developmental timing C_LIO_LIThe architecture of the gene regulatory network defines its trajectory C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/643129v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1faf1aaorg.highwire.dtl.DTLVardef@10053borg.highwire.dtl.DTLVardef@1c76aacorg.highwire.dtl.DTLVardef@12c7b89_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2022

nl-DDM: a non-linear drift-diffusion model accounting for the dynamics of single-trial perceptual decisions

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

neuroscience↗