Biology subjects

Perez-Carrasco, R.

Publications and source records attributed to Perez-Carrasco, R..

At least 19 recordsLinked to original sources

bioRxiv2026

A Bayesian framework reveals heterogeneous and stochastic decision-making in NK cell cytotoxicity

Natural killer (NK) cells display striking variability in their cytotoxic responses, but whether this variation reflects random events, stable differences between cells, or effects of previous encounters remains unclear. We developed a Bayesian framework that uses single-cell interaction histories to disentangle these sources of variability. The framework models target encounters and killing decisions to quantify population heterogeneity, determine the sample sizes needed to distinguish competing mechanisms, and separate stable cell-to-cell differences from history-dependent behaviour. Synthetic data established when these mechanisms can be reliably distinguished in practice. We tested the framework using time-lapse imaging of NK cells exposed to rituximab or CC-96673, an antibody co-targeting CD20 and CD47. Despite similar overall killing, the framework identified distinct underlying responses: rituximab mainly increased mean killing rate, whereas CC-96673 reduced cell-to-cell variation. Event-count analysis supported continuous population heterogeneity, while ordered interaction histories revealed that both stable differences in killing propensity and previous encounters shape cytotoxic decisions. To make the framework directly usable, we introduce BARRACUDA, an open-source web platform and Python package implementing these analyses, including donor-aware extensions, for single-cell cytotoxicity datasets.

immunology↗

bioRxiv2026

ESFS: A Noise-Resilient Framework for Feature Selection and Marker Gene Discovery in Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challenging due to technical noise and batch effects. Most methods for denoising scRNA-seq data have focused on using latent representations such as principal component analysis and deep learning to prioritise biological signals. By contrast, despite its influence on downstream analyses, feature selection has received relatively limited attention, leading to widespread reliance on the comparatively simplistic strategy of highly variable gene selection. Here we present Entropy Sorting Feature Selection (ESFS), a modular, user-friendly framework that substantially improves the interpretability of scRNA-seq data. Notably, ESFS reveals complex expression dynamics that are obscured in latent representations. We demonstrate the utility of ESFS in diverse data: identifying coherent developmental programs across eight independent human embryo datasets without batch integration; resolving spatial gene expression in mouse colon missed by conventional analyses; disambiguating shared and tumour-specific microenvironments in glioblastoma; and disentangling spatial, temporal, and neurogenic programs in the developing mouse neural tube. Beyond delivering a powerful and user-friendly software that deepens insight into complex biological systems, our work establishes Entropy Sorting as a novel information theoretic for advanced data analysis methods.

bioinformatics↗

bioRxiv2025

Protein-protein interactions drive differences in the spatiotemporal dynamics of transcription factors NANOG and SOX2 in naïve pluripotent cells

Maintenance of naive pluripotency requires core transcription factors (TFs) like SOX2 and auxiliary TFs like NANOG, yet molecular mechanisms governing their intra-nuclear dynamics and DNA binding interactions remain unclear. Here, using high-density 3D single-molecule light-field microscopy combined with novel spatiotemporal analysis pipelines, we track SOX2 and NANOG dynamics in live cells. Despite lower protein abundance, NANOG displays a similar chromatin-bound fraction to SOX2. This arises partially because, while both TFs undergo frequent transient non-specific binding interactions ([~]0.5-0.7s), NANOG exhibits more stable specific binding ([~]25s vs [~]16s). Both TFs also assemble into phase-separated domains of [~]400 nm containing both freely diffusing and chromatin-bound proteins, which further influences their dynamics. Strikingly, NANOGs protein-protein interaction domain markedly increases chromatin residence time (>5-fold) and the size of these phase-separated domains. Our work uncovers how NANOG and SOX2 stabilise gene regulatory networks that maintain naive pluripotency while providing quantitative pipelines for dissecting spatiotemporal TF dynamics.

cell biology↗

bioRxiv2025

Epigenetic-metabolic axis in the temporal scaling of mammalian cortical neurogenesis across species

Developmental timescales vary widely across species, with mammalian cortical neurogenesis ranging from just days to several months. Given the conserved laminar architecture and regulatory gene expression sequences, the underlying molecular mechanisms controlling neurogenesis rate remain unknown. Using mouse, ferret, and human models, we combined comparative transcriptomics with mathematical modelling to identify conditions that scale temporal gene expression programs during neurogenesis. We show that H3K27me3-mediated repression is critical for maintaining species-specific neurogenesis timescales, and its ablation scales down neurogenesis duration. Furthermore, we identified the tricarboxylic-acid (TCA)-cycle metabolite -ketoglutarate as a modulator of developmental timing via H3K27me3 demethylation. Together, our findings link epigenetic and metabolic states to the control of species-specific developmental timescales and, ultimately, brain size.

developmental biology↗

bioRxiv2025

Let there be multifunctionality: Uncovering the criticality zoo of the AC-DCgenetic circuit

Gene regulatory networks (GRNs) govern processes such as cell fate, patterning, and adaptation. While multistability and oscillations are both common GRN dynamics in cell biology, they are typically studied and engineered in isolation. Here, we challenge this separation using the AC-DC circuit, a minimal three- gene network that merges the classical toggle switch and repressilator. Using a thermodynamic formalism and Bayesian inference, we show that even a single-inducer version of the circuit can display diverse mul-tifunctional dynamics, including the coexistence of oscillations and multistability. In addition we explore robustness, classify emergent behaviours, and analyse critical slowing down and regime transitions. Re-markably, the AC-DC circuit can produce more than 30 topologically distinct bifurcation diagrams, chal-lenging the classical view that network topology rigidly constrains dynamical outcomes. This flexibility enables synthetic capabilities that couple hysteresis with oscillations, critical slowing down, and reversibility. By uncovering the hidden potential of minimal genetic circuits and outlining design principles for their implementation, this work opens new directions for harnessing emergent complexity using the basic building blocks of life.

systems biology↗

bioRxiv2024

Local Nearby Bifurcations Lead to Synergies in Critical Slowing Down: the Case of Mushroom Bifurcations

The behavior of nonlinear systems close to critical transitions has relevant implications in assessing complex systems stability, transient properties, and resilience. Transient times become extremely long near phase transitions (or bifurcations) in a phenomenon generically known as critical slowing down, observed in electronic circuits, quantum electrodynamics, ferromagnetic materials, ecosystems, and gene regulatory networks. Typically, these transients follow well-defined universal laws of the form{tau} [~] |{micro} - {micro}c| {beta}, describing how their duration,{tau} , varies as the control parameter, {micro}, approaches its critical value, {micro}c. For instance, transients delays right after a saddle-node (SN) bifurcation, influenced by so-called ghosts, follow {beta} = -1/2. Despite intensive research on slowing down phenomena over the past decades for single bifurcations, both local and global, the behavior of transients when several bifurcations are close to each other remains unknown. Here, we study transients close to two SN bifurcations collapsing into a transcritical one. To do so, we analyze a simple nonlinear model of a self-activating gene regulated by an external signal that exhibits a mushroom bifurcation. We also propose and study a normal form for a system with two SN bifurcations merging into a transcritical one. For both systems, we show analytical and numerical evidence of a synergistic increase in transients due to the coupling of the two ghosts and the transcritical slowing down. We also explore the influence of noise on the transients in the gene-regulatory model. We show that intrinsic and extrinsic noise play opposite roles in the slowing down of the transition allowing us to control the timing of the transition without compromising the precision of the timing. This establishes novel molecular strategies to generate genetic timers with transients much larger than the typical timescales of the reactions involved.

systems biology↗

bioRxiv2023

A Mathematical Framework for Measuring and Tuning Tempo in Developmental Gene Regulatory Networks

Embryo development is a dynamic process governed by the regulation of timing and sequences of gene expression, which control the proper growth of the organism. While many genetic programs coordinating these sequences are common across species, the timescales of gene expression can vary significantly among different organisms. Currently, substantial experimental efforts are focused on identifying molecular mechanisms that control these temporal aspects. In contrast, the capacity of established mathematical models to incorporate tempo control while maintaining the same dynamical landscape remains less understood. This manuscript addresses this gap by developing a mathematical framework that links the functionality of developmental programs to the corresponding gene expression orbits (or landscapes). This unlocks the ability to find tempo differences as perturbations in the dynamical system that preserve its orbits. We demonstrate that this framework allows for the prediction of molecular mechanisms governing tempo, through both numerical and analytical methods. Our exploration includes two case studies: a generic network featuring coupled production and degradation, and the repressilator. In the latter, we illustrate how altering the dimerisation rates of transcription factors can decouple the tempo from the shape of the resulting orbits. The manuscript concludes by highlighting how the identification of orthogonal molecular mechanisms for tempo control can inform the design of circuits with specific orbits and tempos.

systems biology↗

bioRxiv2022

Choice of friction coefficient deeply affects tissue behaviour in epithelial vertex models

To understand the mechanisms that coordinate the formation of biological tissues, the use of numerical implementations is necessary. The complexity of such models involves many assumptions and parameter choices that result in unpredictable consequences, obstructing the comparison with experimental data. Here we focus on vertex models, a family of spatial models used extensively to simulate the dynamics of epithelial tissues. Usually, in the literature, the choice of the friction coefficient is not addressed using quasi-static deformation arguments that generally do not apply to realistic scenarios. In this manuscript, we discuss the role that the choice of friction coefficient has on the relaxation times and consequently in the conditions of cell cycle progression and division. We explore the effects that these changes have on the morphology, growth rate, and topological transitions of the tissue dynamics. These results provide a deeper understanding of the role that an accurate mechanical description plays in the use of vertex models as inference tools.

cell biology↗

bioRxiv2022

Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis confounds our understanding of how mutations impact the expression patterns of gene regulatory networks, because such nonlinearities make it difficult to predict the combined phenotypic effects of mutations based on knowledge of the mutations individual effects. This challenge is exacerbated by the dependence of epistasis on the environment, which is particularly germane to gene regulatory networks that interpret signals in space or time. To help fill this knowledge gap, we used the toolkit of synthetic biology to systematically assay the effects of pairwise and triplet combinations of mutant genotypes on the expression pattern of a gene regulatory network expressed in Escherichia coli that interprets an inducer gradient across a spatial domain. We uncovered a preponderance of epistasis in both pairwise and triplet combinations that can switch in magnitude and sign across the inducer gradient to produce a greater diversity of expression pattern phenotypes than would be possible in the absence of such environment-dependent epistasis. We discuss our findings in the context of the evolution of hybrid incompatibilities and evolutionary novelties, arguing that environment-dependent epistasis is likely an important cause of both phenomena in gene regulatory networks.

evolutionary biology↗

bioRxiv2022

Automated design of gene circuits with optimal mushroom-bifurcation behaviour

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation and generic multi-functional bio-circuits. These novel applications require the design of gene circuits leading to sophisticated behaviours and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory of dynamical systems provides powerful tools to address complex nonlinear dynamics and multifunctionality, linking model topology and kinetic parameters with circuit behaviour. However, the challenge of incorporating bifurcation analysis to automated design has not been accomplished so far. In this work we present an optimisation-based method for the automated forward design of synthetic gene circuits with specified bifurcation diagrams, allowing us to find minimal topologies optimizing the required functionalities and taking into account additional requirements and/or context specifications. We apply the method to design of gene circuits exhibiting the so called mushroom bifurcation, a relatively unexplored multi-functional behaviour of particular relevance for developmental biology. Using the results of the optimisation analysis we explore the capabilities of the resulting circuits for possible applications in advanced biosensors, memory devices, and synthetic cell differentiation.

systems biology↗

bioRxiv2022

Levels of p53 expression determine the competitive ability of embryonic stem cells during the onset of differentiation.

During development, the rate of tissue growth is determined by the relative balance of cell division and cell death. Cell competition is a fitness quality control mechanism that contributes to this balance by eliminating viable cells that are less-fit than their neighbours. What mutations confer cells with a competitive advantage or the dynamics of the interactions between winner and loser cells are not well understood. Here, we show that embryonic cells lacking the tumour suppressor p53 are super-competitors that eliminate their wild-type neighbours through the direct induction of apoptosis. This elimination is context dependant, as does not occur when cells are pluripotent and is triggered by the onset of differentiation. Furthermore, by combining mathematical modelling and cell-based assays we show that the elimination of wild-type cells is not through a competition for space or nutrients, but instead is mediated by short range interactions that are dependent on the local cell neighbourhood. This highlights the importance of the local cell neighbourhood and the competitive interactions within this neighbourhood for the regulation of proliferation during early embryonic development.

developmental biology↗

bioRxiv2021

Relaxation time asymmetry in stator dynamics of the bacterial flagellar motor

The bacterial flagellar motor (BFM) is the membrane-embedded rotary molecular motor which turns the flagellum that provides thrust to many bacterial species. This large multimeric complex, composed of a few dozen constituent proteins, has emerged as a hallmark of dynamic subunit exchange. The stator units are inner-membrane ion channels which dynamically bind and unbind to the peptidoglycan at the rotor periphery, consuming the ion motive force (IMF) and applying torque to the rotor when bound. The dynamic exchange is known to be a function of the viscous load on the flagellum, allowing the bacterium to dynamically adapt to its local viscous environment, but the molecular mechanisms of exchange and mechanosensitivity remain to be revealed. Here, by actively perturbing the steady-state stator stoichiometry of individual motors, we reveal a stoichiometry-dependent asymmetry in stator remodeling kinetics. We interrogate the potential effect of next-neighbor interactions and local stator unit depletion and find that neither can explain the observed asymmetry. We then simulate and fit two mechanistically diverse models which recapitulate the asymmetry, finding stator assembly dynamics to be particularly well described by a two-state catch-bond mechanism.

biophysics↗

bioRxiv2021

"Neighborhood watch" model: embryonic epiblast cells assess positional information in relation to their neighbors

In many developing and regenerating systems, tissue pattern is established through gradients of informative morphogens, but we know little about how cells interpret these. Using experimental manipulation of early chick embryos including misexpression of an inducer (VG1 or ACTIVIN) and an inhibitor (BMP4), we test two alternative models for their ability to explain how the site of primitive streak formation is positioned relative to the rest of the embryo. In one model, cells read morphogen concentrations cell-autonomously. In the other, cells sense changes in morphogen status relative to their neighbourhood. We find that only the latter model can account for the experimental results, including some counter-intuitive predictions. This mechanism (which we name "neighbourhood watch" model) illuminates the classic "French Flag Problem" and how positional information is interpreted by a sheet of cells in a large developing system. Summary statementIn a large developing system, the chick embryo before gastrulation, cells interpret gradients of positional signals relative to their neighbours to position the primitive streak, establishing bilateral symmetry.

developmental biology↗

bioRxiv2020

Effects of cell cycle variability on lineage and population measurements of mRNA abundance

Many models of gene expression do not explicitly incorporate a cell cycle description. Here we derive a theory describing how mRNA fluctuations for constitutive and bursty gene expression are influenced by stochasticity in the duration of the cell cycle and the timing of DNA replication. Analytical expressions for the moments show that omitting cell cycle duration introduces an error in the predicted mean number of mRNAs that is a monotonically decreasing function of η, which is proportional to the ratio of the mean cell cycle duration and the mRNA lifetime. By contrast, the error in the variance of the mRNA distribution is highest for intermediate values of η consistent with genome-wide measurements in many organisms. Using eukaryotic cell data, we estimate the errors in the mean and variance to be at most 3% and 25%, respectively. Furthermore, we derive an accurate negative binomial mixture approximation to the mRNA distribution. This indicates that stochasticity in the cell cycle can introduce fluctuations in mRNA numbers that are similar to the effect of bursty transcription. Finally, we show that for real experimental data, disregarding cell cycle stochasticity can introduce errors in the inference of transcription rates larger than 10%.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

Species-specific developmental timing is associated with global differences in protein stability in mouse and human

What determines the pace of embryonic development? Although many molecular mechanisms controlling developmental processes are evolutionarily conserved, the speed at which these operate can vary substantially between species. For example, the same genetic programme, comprising sequential changes in transcriptional states, governs the differentiation of motor neurons in mouse and human, but the tempo at which it operates differs between species. Using in vitro directed differentiation of embryonic stem cells to motor neurons, we show that the programme runs twice as fast in mouse as in human. We provide evidence that this is neither due to differences in signalling, nor the genomic sequence of genes or their regulatory elements. Instead, we find an approximately two-fold increase in protein stability and cell cycle duration in human cells compared to mouse. This can account for the slower pace of human development, indicating that global differences in key kinetic parameters play a major role in interspecies differences in developmental tempo.

developmental biology↗

bioRxiv2019

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch

The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit.

synthetic biology↗

bioRxiv2019

Precision of Tissue Patterning is Controlled by Dynamical Properties of Gene Regulatory Networks

During development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

developmental biology↗

bioRxiv2019

Neuronal differentiation affects tissue mechanics and progenitor arrangement in the vertebrate neuroepithelium

Cell division, movement and differentiation contribute to pattern formation in developing tissues. This is the case in the vertebrate neural tube where neurons differentiate in a characteristic pattern from a highly dynamic proliferating pseudostratified epithelium. To investigate how progenitor proliferation and differentiation affect cell arrangement and growth of the neural tube, we use experimental measurements to develop a mechanical model of the apical surface of the neuroepithelium that incorporates inter-kinetic nuclear movement and spatially varying rates of neuronal differentiation. Simulations predict that tissue growth and the shape of lineage-related clones of cells differ with the rate of differentiation. Growth is isotropic in regions of high differentiation, but dorsoventrally biased in regions of low differentiation. This is consistent with experimental observations. The absence of directional signalling in the simulations indicates that global mechanical constraints are sufficient to explain the observed differences in anisotropy. This provides insight into how the tissue growth rate affects cell dynamics and growth anisotropy and opens up possibilities to study the coupling between mechanics, pattern formation and growth in the neural tube.

developmental biology↗