Biology subjects

Nieto, C.

Publications and source records attributed to Nieto, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Insulators As Dynamic, Tunable Regulators of Enhancer-Promoter Coordination in Living Drosophila Embryos

The three-dimensional organization of the genome enables enhancers and promoters to interact across vast distances and direct transcription. Yet whether architectural elements, such as insulators, serve as rigid, passive barriers or as dynamic, active organizers of this communication remains unclear. Here, using single-cell, live imaging of a Drosophila transgene in which a single enhancer regulates two equidistant promoters, we confirm that the enhancer engages both promoters simultaneously and show that coordinated bursting is intrinsically more productive than uncoordinated activity. Flanking this system with insulators increases coordinated bursting frequency and transcriptional output, indicating that insulator-mediated looping promotes multi-way enhancer-promoter interaction. Further, bidirectionally-paired, homotypic insulators produce stronger coordination than unidirectional pairs. Inserting an intermediate insulator to generate competing loop configurations, together with two-state promoter modeling, we show that these chromatin loops are highly dynamic. This work reframes insulators as active, tunable regulators that shape the frequency, coordination, and productivity of enhancer-promoter communication.

molecular biology↗

bioRxiv2026

The role of cell growth rate on accumulation of the mitotic cyclin Cdc13 in fission yeast

Eukaryotic cells control their size by coordinating growth and division. Fission yeast divide at a reproducible cell size due to regulated activation of the cyclin-dependent kinase Cdk1. The nuclear concentration of mitotic cyclin Cdc13 increases in a time-dependent manner to promote Cdk1 activation as cells grow. Here, we show that interphase Cdc13 is stable against degradation and nuclear export, but is diluted by cell growth. Low glucose reduced cell growth rate but not time-dependent accumulation of Cdc13. Uncoupling the rates of cell growth and Cdc13 accumulation resulted in higher concentrations of nuclear Cdc13 despite reduced cell size. This change coincided with reduced activating phosphorylation of Cdk1-T167. Mathematical modeling and experiments showed that cells maintain size homeostasis under these conditions. In contrast to low glucose, poor nitrogen reduced both cell growth rate and Cdc13 accumulation rate. Therefore, Cdc13 accumulation is independent of cell growth rate but can be altered by nutrient-specific mechanisms.

cell biology↗

bioRxiv2025

Enhancer Placement Impacts Transcriptional Dynamics in Drosophila Embryos

The functional role of enhancer-promoter configurations in transcription regulation remains poorly understood, despite the wide range of linear genomic distance and relative enhancer positioning observed in endogenous contexts. While canonical models suggest that enhancers operate independently of genomic position, technical limitations have obscured insights on transcriptional kinetics. Here, we employ single-cell MS2/MCP-based live imaging in Drosophila embryos to systematically analyze transcriptional dynamics driven by sub-10kb enhancer-promoter arrangements. Kinetic analyses reveal that while linear enhancer-promoter distance moderately tunes transcriptional output, downstream enhancer positioning reduces mRNA output by 70%. Each configuration modulates distinct transcriptional parameters: linear distance governs initiation kinetics, while relative enhancer positioning dictates transcriptional stability. These effects are consistent across varied enhancer and reporter sequences, revealing configuration-dependent cis-regulatory element arrangement as an intrinsic mechanism for transcriptional fine-tuning. This work challenges the paradigm of configuration-independent enhancer function and establishes a framework to dissect the interplay between genome architecture and trans-acting factors.

molecular biology↗

bioRxiv2025

Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development

Nascent RNA synthesis often occurs in periods of high transcriptional activity, interspersed with basal or no activity periods. This phenomenon, known as transcriptional bursting, drives high intercellular variability in gene expression levels. How do key patterning genes in early Drosophila melanogaster embryos overcome this variability to establish precise spatial patterns for tissue development? To address this question, we study single cell transcriptional activity from MS2-based live imaging data of four transgenic constructs (rho, Kr, sna shadow, sna proximal enhancers) and the endogenous eve gene. We developed an algorithm to infer promoter states in hundreds of cells within the embryo using transcriptional activity data. Results show that while mean transcription levels exhibit spatial gradients, the burst duration and interburst timing remain surprisingly invariant across the embryo and different constructs. The time between consecutive bursts was consistent with a memoryless exponential distribution, whereas the burst duration exhibited tighter control with lesser stochastic variations. Our analysis identified two regulatory mechanisms for gene expression gradients: (1) similar burst-timing statistics across genes, with the activity time (the time from the first to the last burst) being modulated to regulate distinct expression levels; (2) for the same gene with different enhancers (sna shadow and sna proximal), we observed changes in the mean burst duration and variability of the inter-burst timing. This study provides a comprehensive approach to analyzing transcriptional bursting kinetics, revealing activity time as a major regulator of spatiotemporal expression patterning in early embryonic development.

systems biology↗

bioRxiv2024

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume {approx} 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

microbiology↗

bioRxiv2024

A Generalized Adder mechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation

Measurements of cell size dynamics have revealed phenomeno-logical principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.

systems biology↗

bioRxiv2024

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

systems biology↗

bioRxiv2023

Coupling Cell Size Regulation and Proliferation Dynamics of C.glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

systems biology↗

bioRxiv2023

Mechanisms of Cell Size Regulation in Slow-Growing Escherichia coli Cells: Discriminating Models Beyond the Adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: precursor protein degradation, nonlinear accumulation rate, and a threshold size termed the commitment size. These models fit mean trends but predict different distributions given the birth size. To validate these models, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. the degradation model could explain the division strategy for media where cells are larger, while the commitment size model could account for smaller cells. The power-law model, finally, better fits the data at intermediate regimes.

systems biology↗

bioRxiv2023

Mixed-Feedback Architectures for Precise Event Timing Through Stochastic Accumulation of Biomolecules

The timing of biochemical events is often determined by the accumulation of a protein or chemical species to a critical threshold level. In a stochastic model, we define event timing as the first-passage time for the level to cross the threshold from zero or random initial conditions. This first-passage time can be modulated by implementing feedback in synthesis, that is, making the production rate an arbitrary function of the current species level. We aim to find the optimal feedback strategy that reduces the timing noise around a given mean first-passage time. Previous results have shown that while a no-feedback strategy (i.e., an independent constant production rate) is optimal in the absence of degradation and zero-molecules initial condition, a negative feedback is optimal when the process starts at random initial conditions. We show that when the species can be degraded and the synthesis rates are set to depend linearly on the number of molecules, a positive feedback strategy (the production rate increases with the level of the molecule) minimizes timing noise. However, if no constraints on the feedback are imposed, the optimal strategy involves a mixed feedback approach, which consists of an initial positive feedback followed by a sharp negative feedback (the production rate decreases with the level) near the threshold. Finally, we quantify the fundamental limits of timing noise reduction with and without feedback control when time-keeping species are subject to degradation.

systems biology↗

bioRxiv2023

Comparative analysis of noise-attenuation mechanisms in gene expression: From single cells to cell populations

Negative feedback regulation is a well-known motif for suppressing deleterious fluctuations in gene product levels. We systematically compare two scenarios where negative feedback is either implemented in the protein production rate (regulated synthesis) or in the protein degradation rate (regulated degradation). Our results show that while in lownoise regimes both schemes are identical, they begin to show remarkable differences in high-noise regimes. Analytically solving for the probability distributions of the protein levels reveals that regulated synthesis is a better strategy to suppress random fluctuations while also minimizing protein levels dipping below a threshold. In contrast, regulated degradation is preferred if the goal is to minimize protein levels going beyond a threshold. Finally, we compare and contrast these distributions not only in a single cell over time but also in an expanding cell population where these effects can be buffered or exacerbated due to the coupling between expression and cell growth.

systems biology↗

bioRxiv2023

Optimal harvesting strategies for ecological population dynamics

What is the optimal way to harvest an ecological population sustainably is a fundamental problem in natural resource management. Here we use the framework of the stochastic logistic model which captures random birth-death of individuals to determine the optimal harvesting strategy that maximizes the integrated yield over time. Harvesting is assumed to occur at either a constant or state-dependent rate, and individuals are harvested with a certain probability whenever a harvesting event occurs. A special case of state-dependent harvesting is a threshold-based strategy, where harvesting is done when the population crosses a threshold. We use moment closure schemes to develop analytical formulas quantifying the mean and optimal yield. Moreover, as populations are susceptible to extinction at high harvesting rates, the Finite State Projection (FSP) method is used to estimate the probabilities of extinction across strategies and parameter regimes. Our results show that the threshold-based strategy is most effective in maximizing the yield as it suppresses population fluctuations and minimizes extinction events.

ecology↗

bioRxiv2023

Stochastic dynamics of ecological populations subjected to environmental perturbations

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield.

ecology↗

bioRxiv2022

Quantifying noise modulation from coupling of stochastic expression to cellular growth: An analytical approach

The overexpression of many proteins can often have a detrimental impact on cellular growth. This expression-growth coupling leads to positive feedback - any increase of intracellular protein concentration reduces the growth rate of cell size expansion that in turn enhances the concentration via reduced dilution. We investigate how such feedback amplifies intrinsic stochasticity in gene expression to drive a skewed distribution of the protein concentration. Our results provide an exact solution to this distribution by analytically solving the Chapman-Kolmogorov equation, and we use it to quantify the enhancement of noise/skewness as a function of expression-growth coupling. This analysis has important implications for the expression of stress factors, where high levels provide protection from stress, but come at the cost of reduced cellular proliferation. Finally, we connect these analytical results to the case of an actively degraded gene product, where the degradation machinery is working close to saturation.

systems biology↗

bioRxiv2022

The role of division stochasticity on the robustness of bacterial size dynamics

Variables of bacterial division such as size at birth, growth rate, division time, and the position of the septal ring, all vary from cell to cell. Currently, it is unknown how these random fluctuations can combine to produce a robust mechanism of homeostasis. To address this question, we studied the dynamics of the cell division process from both experimental and theoretical perspectives. Our model predicts robustness in division times as sustained oscillations in metrics of the cell size distribution, such as the mean, variability, and the cell size autocorrelation function. These oscillations do not get damped, even considering stochasticity in division timing and the cell size at the beginning of the experiment. Damping appears just after inducing stochasticity in either the septum position or the growth rate. We compare the predictions of the full model with the size dynamics of E. coli bacteria growing in minimal media using either glucose or glycerol as carbon sources. We observe that growth in poorer media increases the noise in both partitioning position and growth rate. This additional noise results in oscillations with more damping. Although intracellular noise is known as a source of phenotypic variation, our results show that it can play a similar but subtler role in maintaining population-level homeostasis by causing rapid desynchronization of cell cycles..

systems biology↗

bioRxiv2022

Cell size regulation and proliferation fluctuations in single-cell derived colonies

Exponentially growing cells regulate their size by controlling their timing of division. Since two daughter cells are born as a result of this cell splitting, cell size regulation has a direct connection with cell proliferation dynamics. Recent models found more clues about this connection by suggesting that division occurs at a size-dependent rate. In this article, we propose a framework that couples the stochastic transient dynamics of both the cell size and the number of cells in the initial expansion of a single-cell-derived colony. We describe the population from the two most common perspectives. The first is known as Single Lineage: where only one cell is followed in each colony, and the second is Population Snapshots: where all cells in different colonies are followed. At a low number of cells, we propose a third perspective; Single Colony, where one tracks only cells with a common ancestor. We observe how the statistics of these three approaches are different at low numbers and how the Single Colony perspective tends to Population Snapshots at high numbers. Analyzing colony-to-colony fluctuations in the number of cells, we report an intriguing find: the extent of fluctuations first increases with time and then decreases to approach zero at large numbers of cells. In contrast, in classical size-independent proliferation models, where cell division occurs based on a pure timing mechanism, fluctuations in cell number increase monotonically over time to approach a nonzero value. We systematically study these differences and the convergence speed using different size control strategies.

systems biology↗

bioRxiv2022

Threshold-crossing time statistics for gene expression in growing cells

Many intracellular events are triggered by attaining critical concentrations of their corresponding regulatory proteins. How cells ensure precision in the timing of the protein accumulation is a fundamental problem, and contrasting predictions of different models can help us understand the mechanisms involved in such processes. Here, we formulate the timing of protein threshold-crossing as a first passage time (FPT) problem focusing on how the mean FPT and its fluctuations depend on the threshold protein concentration. First, we model the protein-crossing dynamics from the perspective of three classical models of gene expression that do not explicitly accounts for cell growth but consider the dilution as equivalent to degradation: (birth-death process, discrete birth with continuous deterministic degradation, and Fokker-Planck approximation). We compare the resulting FPT statistics with a fourth model proposed by us (growing cell) that comprises size-dependent expression in an exponentially growing cell. When proteins accumulate in growing cells, their concentration reaches a steady value. We observe that if dilution by cell growth is modeled as degradation, cells can reach concentrations higher than this steady-state level at a finite time. In the growing cell model, on the other hand, the FPT moments diverge if the threshold is higher than the steady-state level. This effect can be interpreted as a transition between noisy dynamics when cells are small to an almost deterministic behavior when cells grow enough. We finally study the mean FPT that optimizes the timing precision. The growing cell model predicts a higher optimal FPT and less variability than the classical models.

systems biology↗

bioRxiv2022

Regulatory strategies to schedule threshold crossing of protein levels at a prescribed time

The timing of diverse cellular processes is based on the instant when the concentration of regulatory proteins crosses a critical threshold level. Hence, noise mechanisms inherent to these protein synthesis pathways drive statistical fluctuations in such events timing. How to express proteins ensuring both the threshold crossing at a prescribed time and minimal timing fluctuations? To find this optimal strategy, we formulate a model where protein molecules are synthesized in random bursts of gene activity. The burst frequency depends on the protein level creating a feedback loop, and cellular growth dilutes protein concentration between consecutive bursts. Counterintuitively, our analysis shows that positive feedback in protein production is best for minimizing variability in threshold-crossing times. We analytically predict the optimal feedback strength in terms of the dilution rate. As a corollary to our result, a no-feedback strategy emerges as the optimal strategy in the absence of dilution. We further consider other noise sources, such as randomness in either the initial condition or the threshold level, and find that in many cases, we need either strongly negative or positive feedback for precise scheduling for events.

systems biology↗