Biology subjects

Nava-Sedeno, J. M.

Publications and source records attributed to Nava-Sedeno, J. M..

4 recordsLinked to original sources

bioRxiv2026

How host self-regulation governs hyperparasitoid persistence in a caterpillar-parasitoid-trigonalid system

Species at the highest trophic levels are generally the least resilient to perturbations, yet the factors that determine their persistence remain poorly understood. We ask how the intrinsic self-regulation of a basal host governs the persistence of the trophic levels above it. To address this question, we use a minimal discrete-time model of a host-parasitoid-hyperparasitoid system motivated by the biology of trigonalid wasps, obligate hyperparasitoids that complete development only when their caterpillar host is also attacked by a primary parasitoid. We compare two forms of host self-regulation in the absence of natural enemies: scramble (Ricker) fecundity, appropriate when the parasitoid is the host's principal enemy and which recovers the classical model of Beddington and Hammond as a special case; and contest (Beverton-Holt) fecundity, appropriate when the host is held in check by many additional enemies. Using numerical continuation, Lyapunov exponents, and direct computation of the model's trajectories, we find that the form of host regulation is decisive for persistence at the top trophic level. Under scramble host dynamics, coexistence is extremely fragile with respect to both parameter values and initial conditions. Under contest host dynamics, coexistence is robust across broad parameter ranges and loses stability only gradually, through a supercritical Neimark--Sacker bifurcation and a subsequent torus-doubling route to chaos. Therefore, in at least some trophic networks, the resilience of the highest levels can be determined at the base of the network.

ecology↗

bioRxiv2025

On the onset of multicellular invasive behavior in hierarchical lineage: the role of inhibitory feedback and local fluctuations

The emergence of multicellular invasive behavior is a key characteristic of various biological processes, including wound healing, development, and tissue regeneration. In this study, we develop a lattice-gas cellular automaton (LGCA) model to explore the role of inhibitory feedback in the invasive behavior of a hierarchical lineage composed of stem cells and differentiated cells. We consider both non-spatial and spatial stochastic models to investigate how spatial interactions influence invasion dynamics. Our findings suggest that inhibitory feedback from differentiated cells significantly impacts the invasive potential of stem cells. In addition, local fluctuations induce unstable fronts that move with relatively low speed. Finally, we explore the implications of our work for understanding the regulation of multicellular dynamics in various pathophysiological contexts.

cancer biology↗

bioRxiv2020

BIO-LGCA: a cellular automaton modelling class for analysing collective cell migration

1Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics - understood as population behaviour arising from the interplay of the constituting discrete cells - can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments. A Python package which implements various interaction rules and visualisations of BIO-LGCA model simulations we have developed is available at https://github.com/sisyga/BIO-LGCA. 2 Author summaryPattern formation during embryonic development and pathological tissue dynamics, such as cancer invasion, emerge from individual inter-cellular interactions. In order to study the impact of single cell dynamics and cell-cell interactions on tissue behaviour, one needs to develop space-time-dependent on- or off-lattice agent-based models (ABMs), which consider the behaviour of individual cells. However, classical on-lattice agent-based models also known as cellular automata fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. Here, we present the rule- and lattice-based BIO-LGCA modelling class which allows for (i) rigorous derivation of rules from biophysical laws and/or experimental data, (ii) mathematical analysis of collective migration, and (iii) computationally efficient simulations.

bioinformatics↗

bioRxiv2018

Hook length of the bacterial flagellum is controlled to nanometer-scale for optimal motility performance

Most bacteria swim in liquid environments by rotating one or several flagella. The long external filament of the flagellum is connected to a membrane-embedded basal-body by a flexible universal joint, the hook, which allows the transmission of motor torque to the filament. The length of the hook is controlled on a nanometer-scale by a sophisticated molecular ruler mechanism. However, why its length is stringently controlled has remained elusive. We engineered and studied a diverse set of hook-length variants of Salmonella enterica. Measurements of plate-assay motility, single-cell swimming speed and directional persistence in quasi 2D and population-averaged swimming speed and body angular velocity in 3D revealed that the motility performance is optimal around the wild type hook-length. We conclude that too short hooks may be too stiff to function as a junction and too long hooks may buckle and create instability in the flagellar bundle. Accordingly, peritrichously flagellated bacteria move most efficiently as the distance travelled per body rotation is maximal and body wobbling is minimized. Thus, our results suggest that the molecular ruler mechanism evolved to control flagellar hook growth to the optimal length consistent with efficient bundle formation. The hook-length control mechanism is therefore a prime example of how bacteria evolved elegant, but robust mechanisms to maximize their fitness under specific environmental constraints.\n\nAuthor summaryMany bacteria use flagella for directed movement in liquid environments. The flexible hook connects the membrane-embedded basal-body of the flagellum to the long, external filament. Flagellar function relies on self-assembly processes that define or self-limit the lengths of major parts. The length of the hook is precisely controlled on a nanometer-scale by a molecular ruler mechanism. However, the physiological benefit of tight hook-length control remains unclear. Here, we show that the molecular ruler mechanism evolved to control the optimal length of the flagellar hook, which is consistent with efficient motility performance. These results highlight the evolutionary forces that enable flagellated bacteria to optimize their fitness in diverse environments and might have important implications for the design of swimming micro-robots.

microbiology↗