Biology subjects

Gelens, L.

Publications and source records attributed to Gelens, L..

15 recordsLinked to original sources

bioRxiv2026

Geometry shapes cytoplasmic Cdk1 waves that drive cortical dynamics

Cell division in large embryos is coordinated by spatial waves of Cyclin B-Cdk1 activity that spread through the cytoplasm and drive cortical contractility. However, it remains unclear how cell size and nuclear enrichment of Cyclin B-Cdk1 shape these waves, and how the cytoplasmic signal is transmitted to the cortex. Here we couple established reaction-diffusion models of Cyclin B-Cdk1 signaling and the excitable Rho-actin cortex to ask how cell geometry and elevated nuclear Cyclin B-Cdk1 concentration shape cytoplasmic wave propagation in spherical cells. We find that cytoplasmic waves consist of two distinct parts: an activation front that propagates in a manner consistent with trigger-wave behavior, and a wave back controlled by concentration gradients in the cell cycle oscillator. Because these parts are generated by different mechanisms, they can move at different speeds or even in opposite directions, depending on nuclear size and effective cell size. This yields a simple geometric explanation for why surface contraction waves propagate in opposite directions in starfish oocytes and Xenopus embryos, without invoking system-specific cortical mechanisms. Coupling the Cdk1 signal to the cortex further shows how cytoplasmic waveforms regulate Rho-actin dynamics through inhibition of the Rho GEF Ect2. Together, these results provide a mechanistic framework linking localized nuclear activation, cytoplasmic Cdk1 waves, and cortical responses in large embryonic cells.

cell biology↗

bioRxiv2025

Beyond Arrhenius: Nonlinear and negative temperature scaling of biological rates from multi-step mechanisms

Temperature shapes all biological processes, particularly during the early development of ectothermic organisms. A widely used framework for describing temperature dependence is the Arrhenius equation, which predicts an exponential increase in rates with temperature. However, biological rates often deviate from this prediction when measured across broader temperature ranges. While negative apparent activation energies are often attributed to protein denaturation, this cannot explain similar behavior observed at temperatures where enzymes remain stable. These broader scaling patterns remain mechanistically unexplained. Here we present a general Markov chain framework for modeling biological timing as cascades of reversible, temperature-dependent steps. Applying this model to 121 published datasets spanning diverse species, biological timescales, and temperature ranges, we find that a consistent three-zone scaling pattern emerges: Arrhenius-like behavior at low and high temperatures, and a quadratic exponential regime at intermediate temperatures. We show that this pattern arises naturally from differences in activation energies across steps in the network. The quadratic exponential regime is an emergent feature of averaging across many steps and is robust to variation across network realizations. In contrast, Arrhenius-like scaling at the extremes tends to be more variable and originates from smaller sub-networks. Apparent negative activation energies can emerge naturally from the dynamics of multi-step networks, even in the absence of protein denaturation. Our framework provides a unified mechanistic explanation for diverse temperature-scaling behaviors in biology and may help predict how developmental and physiological processes respond to environmental change. Although we focus on development, the model is broadly applicable to biological systems governed by multi-step reaction networks.

systems biology↗

bioRxiv2024

Mechanistic origins of temperature scaling in the early embryonic cell cycle

Temperature profoundly impacts organismal physiology and ecological dynamics, particularly affecting ectothermic species and making them especially vulnerable to climate changes. Although complex physiological processes usually involve dozens of enzymes, empirically it is found that the rates of these processes often obey the Arrhenius equation, which was originally proposed for individual chemical reactions. Here we have examined the temperature scaling of the early embryonic cell cycle, with the goal of understanding why the Arrhenius equation approximately holds and why it breaks down at temperature extremes. Using experimental data from Xenopus laevis, Xenopus tropicalis, and Danio rerio, plus published data from Caenorhabditis elegans, Caenorhabditis briggsae, and Drosophila melanogaster, we find that the apparent activation energies (Ea values) for the early embryonic cell cycle for diverse ectotherms are all similar, 75 {+/-} 7 kJ/mol (mean {+/-} std.dev., n = 6), which corresponds to a Q10 value at 20{degrees}C of 2.8 {+/-} 0.2 (mean {+/-} std.dev., n = 6). Using computational models, we find that the approximate Arrhenius scaling and the deviations from it at high and low temperatures can be accounted for by biphasic temperature scaling in critical individual components of the cell cycle oscillator circuit, by imbalances in the Ea values for different partially rate-determining enzymes, or by a combination of both. Experimental studies of cycling Xenopus extracts indicate that both of these mechanisms contribute to the general scaling of temperature, and in vitro studies of individual cell cycle regulators confirm that there is in fact a substantial imbalance in their Ea values. These findings provide mechanistic insights into the dynamic interplay between temperature and complex biochemical processes, and into why biological systems fail at extreme temperatures.

systems biology↗

bioRxiv2024

The nuclear-cytoplasmic ratio controls the cell cycle period in compartmentalized frog egg extract

Each proliferating cell replicates its DNA and internal components before distributing this material evenly to its daughters. Although the regulation of cyclin-dependent kinases (Cdks) that dictate orderly cell cycle progression is well characterized, how the subcellular localization of the cell cycle machinery contributes to timing is not well understood. We investigated the influence of the nucleus by reconstituting cell cycle oscillations in droplets of frog egg extract in the absence or presence of a nuclear compartment and monitoring dynamics by time-lapse microscopy. We found that the cell cycle time increased in the presence of nuclei, which grew larger with each cell cycle. The correlation between increasing nuclear volume and a longer cell cycle period was maintained across extracts and nuclei from various Xenopus species and persisted upon inhibition of DNA replication or transcription. However, inhibition of nuclear import or the kinase Wee1 impacted the relationship between the nuclear-cytoplasmic ratio and the cell cycle period. To conceptually capture these experimental observations, we developed a computational model that incorporates cell cycle oscillations, nuclear-cytoplasmic compartmentalization, and periodic nuclear envelope breakdown and reformation. Altogether, our results support the major role of the nuclear compartment in setting the pace of the cell cycle and provide an explanation for the increase in cell cycle length observed at the midblastula transition when cells become smaller and the nuclear-cytoplasmic ratio increases.

biophysics↗

bioRxiv2024

Mitotic waves in frog egg extracts: Transition from phase waves to trigger waves

Cyclin-dependent kinase 1 (Cdk1) activity rises and falls throughout the cell cycle, a cell-autonomous process known as mitotic oscillations. These oscillators can synchronize when spatially coupled, providing a crucial foundation for rapid synchronous divisions in large early embryos like Drosophila ([~] 0.5 mm) and Xenopus ([~] 1.2 mm). While diffusion alone cannot achieve such long-range coordination, recent studies have proposed two types of mitotic waves, phase and trigger waves, to explain the phenomena. How the waves establish over time for efficient spatial coordination remains unclear. Using Xenopus laevis egg extracts and a Cdk1 FRET sensor, we observe a transition from phase waves to a trigger wave regime in an initially homogeneous cytosol. Adding nuclei accelerates such transition. Moreover, the system transitions almost immediately to this regime when externally driven by metaphase-arrested extracts from the boundary. Employing computational modeling, we pinpoint how wave nature, including speed-period relation, depends on transient dynamics and oscillator properties, suggesting that phase waves appear transiently due to the time required for trigger waves to entrain the system and that spatial heterogeneity promotes entrainment. Therefore, we show that both waves belong to a single biological process capable of coordinating the cell cycle over long distances.

biophysics↗

bioRxiv2023

Data-driven discovery of oscillator models using SINDy: Towards the application on experimental data in biology

Periodic changes in the concentration or activity of different molecules regulate vital cellular processes such as cell division and circadian rhythms. Developing mathematical models is essential to better understand the mechanisms underlying these oscillations. Recent data-driven methods like SINDy have fundamentally changed model identification, yet their application to experimental biological data remains limited. This study investigates SINDys constraints by directly applying it to biological oscillatory data. We identify insufficient resolution, noise, dimensionality, and limited prior knowledge as primary limitations. Using various generic oscillator models of different complexity and/or dimensionality, we systematically analyze these factors. We then propose a comprehensive guide for inferring models from biological data, addressing these challenges step by step. Our approach is validated using glycolytic oscillation data from yeast.

bioinformatics↗

bioRxiv2023

The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms

Many biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay, of double regulation (acting on production and degradation), and of enzymatic degradation.

systems biology↗

bioRxiv2023

Cell cycle oscillations driven by two interlinked bistable switches

Regular transitions between interphase and mitosis during the cell cycle are driven by changes in the activity of the enzymatic protein complex cyclin B with cyclin-dependent kinase 1 (Cdk1). At the most basic level, this cell cycle oscillator is driven by negative feedback: active cyclin B Cdk1 activates the Anaphase-Promoting Complex - Cyclosome, which triggers the degradation of cyclin B. Such cell cycle oscillations occur fast and periodically in the early embryos of the frog Xenopus laevis, where several positive feedback loops leading to bistable switches in parts of the regulatory network have been experimentally identified. Here, we build cell cycle oscillator models to show how single and multiple bistable switches in parts of the underlying regulatory network change the properties of the oscillations and how they can confer robustness to the oscillator. We present a detailed bifurcation analysis of these models.

systems biology↗

bioRxiv2021

Immunogenomic, single-cell and spatial dissection of CD8+T cell exhaustion reveals critical determinants of cancer immunotherapy

Tumoural-CD8+T cells exhibit exhausted or dysfunctional states. Contrary to immunotherapy-responsive exhausted-CD8+T cells, the clinical features of dysfunctional-CD8+T cells are disputed. Hence, we conducted large-scale multi-omics and multi-dimensional mapping of CD8+T cell-states across multiple cancer patient-cohorts. This identified tumour-specific continuum of CD8+T cell-states across 6 human cancers, partly imprinted by organ-specific immuno-modulatory niches. Herein, melanoma and glioblastoma enriched prototypical exhausted (CD8+TEXT) and severely-dysfunctional (CD8+TSDF) states, respectively. Contrary to CD8+TEXT, CD8+TSDF displayed transcriptomic and epigenetic effector/cytolytic dysfunctions, and dysregulated effector/memory single-cell trajectories, culminating into maladaptive prodeath stress and cell-cycle defects. Suboptimal antigen-priming underscored CD8+TSDF, which was distinct from immune-checkpoints "rich" CD8+TEXT, reflecting chronic antigen-stimulation. Continuum variation also existed on tumour spatial-level, with convergent (CD8+TEXT-supportive vascular regions) and divergent features (dysfunctional CD4+T::CD8+TSDFcell-to-cell interactions) between melanoma and glioblastoma. Globally, IFN{gamma}-IL2 disparities, paucity of intra-tumoural CD4+/CD8+T cells, and myeloid TGF{beta}/wound healing responses, distinguished CD8+TSDF-landscape. Within immuno-oncology clinical-trials, anti-PD1 immunotherapy failed to "reinvigorate" CD8+TSDF-landscape, and instead facilitated effector-dysfunction and TGF{beta}/wound healing. However, cellular immunotherapies (dendritic cell-vaccines, adoptive T-cell therapy) ameliorated assorted CD8+TSDF-landscape disparities, highlighting a roadmap for anti-glioblastoma multimodal-immunotherapy. Collectively, our study comprehensively expands clinical-knowledge on CD8+T cell-exhaustion and suggests that tumour-specific, pre-existing CD8+TEXT/TSDF-states, determine immunotherapy-responses.

immunology↗

bioRxiv2021

A modular approach for modeling the cell cycle based on functional response curves

Modeling biochemical reactions by means of differential equations often results in systems with a large number of variables and parameters. As this might complicate the interpretation and generalization of the obtained results, it is often desirable to reduce the complexity of the model. One way to accomplish this is by replacing the detailed reaction mechanisms of certain modules in the model by a mathematical expression that qualitatively describes the dynamical behavior of these modules. Such an approach has been widely adopted for ultrasensitive responses, for which underlying reaction mechanisms are often replaced by a single Hill equation. Also time delays are usually accounted for by using an explicit delay in delay differential equations. In contrast, however, S-shaped response curves, which are often encountered in bistable systems, are not easily modeled in such an explicit way. Here, we extend the classical Hill function into a mathematical expression that can be used to describe both ultrasensitive and S-shaped responses. We show how three ubiquitous modules (ultrasensitive responses, S-shaped responses and time delays) can be combined in different configurations and explore the dynamics of these systems. As an example, we apply our strategy to set up a model of the cell cycle consisting of multiple bistable switches, which can account for events such as DNA damage and coupling to the circadian clock in a phenomenological way. Author summaryBistability plays an important role in many biochemical processes and typically emerges from complex interaction patterns such as positive and double negative feedback loops. Here, we propose to theoretically study the effect of bistability in a larger interaction network. We explicitly incorporate a functional expression describing an S-shaped input-output curve in the model equations, without the need for considering the underlying biochemical events. This expression can be converted into a functional module for an ultrasensitive response, and a time delay is easily included as well. Exploiting the fact that several of these modules can easily be combined in larger networks, we construct a cell cycle model consisting of multiple bistable switches and show how this approach can account for a number of known properties of the cell cycle.

systems biology↗

bioRxiv2020

Bistable, biphasic regulation of PP2A-B55 accounts for the dynamics of mitotic substrate phosphorylation

The phosphorylation of mitotic proteins is bistable, which contributes to the decisiveness of the transitions into and out of M phase. The bistability in substrate phosphorylation has been attributed to bistability in the activation of the cyclin-dependent kinase Cdk1. However, more recently it has been suggested that bistability also arises from positive feedback in the regulation of the Cdk1-counteracting phosphatase, PP2A-B55. Here, we demonstrate biochemically using Xenopus laevis egg extracts that the Cdk1-counteracting phosphatase PP2A-B55 functions as a bistable switch, even when the bistability of Cdk1 activation is suppressed. In addition, Cdk1 regulates PP2A-B55 in a biphasic manner; low concentrations of Cdk1 activate PP2A-B55 and high concentrations inactivate it. As a consequence of this incoherent feedforward regulation, PP2A-B55 activity rises concurrently with Cdk1 activity during interphase and suppresses substrate phosphorylation. PP2A-B55 activity is then sharply downregulated at the onset of mitosis. During mitotic exit Cdk1 activity initially falls with no obvious change in substrate phosphorylation; dephosphorylation then commences once PP2A-B55 spikes in activity. These findings suggest that changes in Cdk1 activity are permissive for mitotic entry and exit, but the changes in PP2A-B55 activity are the ultimate trigger.

cell biology↗

bioRxiv2020

Dynamic bistable switches enhance robustness and accuracy of cell cycle transitions

Bistability is a common mechanism to ensure robust and irreversible cell cycle transitions. Whenever biological parameters or external conditions change such that a threshold is crossed, the system abruptly switches between different cell cycle states. Experimental studies indicate that the shape of the bistable response curve changes dynamically in time. Here, we show how such a dynamically changing bistable switch can provide a cell with better control over the timing of cell cycle transitions. Moreover, cell cycle oscillations built on bistable switches are more robust when the bistability is modulated in time. Our results are not specific to cell cycle models and may apply to other bistable systems in which the bistable response curve is time-dependent. Author summaryMany systems in nature show bistability, which means they can evolve to one of two stable steady states under exactly the same conditions. Which state they evolve to depends on where the system comes from. Such bistability underlies the switching behavior that is essential for cells to progress in the cell division cycle. A quick switch happens when the cell jumps from one steady state to another steady state. Typical of this switching behavior is its robustness and irreversibility. In this paper, we expand this viewpoint of the dynamics of the cell cycle by considering bistable switches which themselves are changing in time. This gives the cell an extra layer of control over transitions both in time and in space, and can make those transitions more robust. Such dynamically changing bistability can appear very naturally. We show this in a model of mitotic entry, in which we include a nuclear and cytoplasmic compartment. The activity of a crucial cell cycle protein follows a bistable switch in each compartment, but the shape of its response is changing in time as proteins are imported and exported from the nucleus.

systems biology↗

bioRxiv2019

Mutualistic cross-feeding in microbial systems generates bistability via an Allee effect

Mutualistic interactions are characterized by the positive influence that two species exert on each other. Such mutualism can lead to bistability. Depending on the initial population size species will either survive or go extinct. Various phenomenological models have been suggested to describe bistability in mutualistic systems. However, these models do not account for interaction mediators such as nutrients. In contrast, nutrient-explicit models do not provide an intuitive understanding of what causes bistability. Here, we reduce a theoretical nutrient-explicit model of two mutualistic cross-feeders in a chemostat, uncovering an explicit relation to a growth model with an Allee effect. We show that the dilution rate in the chemostat leads to bistability by turning a weak Allee effect into a strong Allee effect. This happens as long as there is more production than consumption of cross-fed nutrients. Thanks to the explicit relationship of the reduced model with the underlying experimental parameters, these results allow to predict the biological conditions that sustain or prevent the survival of mutualistic species.

ecology↗

bioRxiv2018

Excitable dynamics through toxin-induced mRNA cleavage in bacteria

Toxin-antitoxin (TA) systems in bacteria and archaea are small genetic elements consisting of the genes coding for an intracellular toxin and an antitoxin that can neutralize this toxin. In various cases, the toxins cleave the mRNA. In this theoretical work we use deterministic and stochastic modeling to explain how toxin-induced cleavage of mRNA in TA systems can lead to excitability, allowing large transient spikes in toxin levels to be triggered. By using a simplified network where secondary complex formation and transcriptional regulation are not included, we show that a two-dimensional, deterministic model captures the origin of such toxin excitations. Moreover, it allows to increase our understanding by examining the dynamics in the phase plane. By systematically comparing the deterministic results with Gillespie simulations we demonstrate that even though the real TA system is intrinsically stochastic, toxin excitations can be accurately described deterministically. A bifurcation analysis of the system shows that the excitable behavior is due to a nearby Hopf bifurcation in the parameter space, where the system becomes oscillatory. The influence of stress is modeled by varying the degradation rate of the antitoxin and the translation rate of the toxin. We find that stress increases the frequency of toxin excitations and decreases the excitation time. The inclusion of secondary complex formation and transcriptional regulation does not fundamentally change the mechanism of toxin excitations. Therefore, the deterministic model used in this work provides a simple and intuitive explanation of toxin excitations in TA systems.\n\nAuthor summary\n\nThe genomes of most bacteria and archaea encode several toxin-antitoxin (TA) systems, small genetic elements consisting of the genes coding for an intracellular toxin and an antitoxin that can neutralize this toxin. For several toxins, the target is the mRNA. In this theoretical work we use deterministic and stochastic modeling to explain how toxin-induced cleavage of mRNA in TA systems can trigger large transient spikes in toxin levels. By using a simplified network where secondary complex formation and transcriptional regulation are not included, we show that a two-dimensional, deterministic model captures the origin of such toxin excitations. By systematically comparing our findings with more complex models, we find that the intrinsically stochastic TA system shows similar toxin excitations that are accurately predicted by the underlying simplified theory. This has allowed us to understand how the TA system responds in the presence of stress conditions, finding that toxin excitations become shorter and more frequent.

systems biology↗

bioRxiv2018

Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a synthetic gut community

Whereas the composition of the human gut microbiome is relatively well resolved, predictive understanding of its response to perturbations such as diet shifts is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics. We established a synthetic community composed of three representative human gut isolates in well-controlled conditions in vitro. We then explored species interactions by performing all mono- and pair-wise fermentation experiments and quantified with a mechanistic community model how well tri-culture dynamics was predicted from mono-culture data. With the model as a reference, we demonstrated that species grown in co-culture behaved differently than in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and in simulations that dominance in tri-culture sensitively depends on initial conditions. Our work has important implications for gut microbial community modeling as well as ecological interaction detection from batch cultures.

bioinformatics↗