Biology subjects

Guisoni, N.

Publications and source records attributed to Guisoni, N..

5 recordsLinked to original sources

bioRxiv2025

One predator and two competing prey: insights from a stochastic metapopulation model and mean-field equations

We investigate a spatially explicit metapopulation model consisting of one predator and two hierarchically competing prey species on a discrete lattice. Each local population follows stochastic rules for extinction, colonisation, competition, and predation. From the master equation of this individual-based model, we rigorously derive the corresponding mean-field equations. The analysis of these first-principles mean-field equations reveals the existence of a rich phase diagram with different coexistence regimes depending on parameter values. We identify both stable and spiral nodes, in agreement with the damped oscillations observed in Monte Carlo simulations in the latter case. We find qualitative agreement between the mean-field results and Monte Carlo simulations for the three-species coexistence and for the coexistence of the two prey species. However, the mean-field description fails to reproduce the coexistence between the predator and the inferior prey at high predator coverages. We argue that this discrepancy arises from spatial prey aggregation, which the mean-field approach cannot capture since it neglects correlations. In the stochastic model, spatial clustering acts as a crucial protective mechanism against predation, particularly for the best coloniser. Our findings suggest that prey aggregation contributes to system stability when colonisation and predation operate at comparable spatial scales. The combination of first-principles mean-field equations and stochastic simulations constitutes a powerful framework for clarifying the roles of hierarchical interactions, predation, colonisation, spatial organisation and stochasticity in multi-species coexistence.

ecology↗

bioRxiv2024

Unraveling tumor heterogeneity: Quantitative insights from scRNA-seq analysis in breast cancer subtypes

Tumors are complex systems characterized by genetic, transcriptomic, phenotypic, and microenvironmental variations. The complexity of this heterogeneity plays a crucial role in metastasis, tumor progression, and recurrence. In this work, we utilized publicly available single-cell transcriptomics data from human breast cancer samples (ER+, HER2+, and triple-negative) to evaluate key concepts pertinent to cancer biology. Quantitative assessments included measures based on copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and different protein-protein interaction networks (PPINs). We found that entropy and PPIN activity related to the cell cycle delineate cell clusters with notably elevated mitotic activity, particularly elevated in aggressive breast cancer subtypes. Additionally, CNA distributions differentiate between ER+ and HER2+/TN subtypes. Further, we identified positive correlations among the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as basal and mesenchymal cell lines. These scores reveal associations with tumor characteristics, reflecting the known malignancy spectrum across breast cancer subtypes. By bridging the gap between existing literature and a comprehensive quantitative approach, we present a novel framework for quantifying cancer traits from scRNA-seq data by establishing several scores. This approach highlights the potential for deeper insights into tumor biology compared to conventional marker-based approaches.

bioinformatics↗

bioRxiv2022

ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is for each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT [1] and CytoTRACE [2], for four data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/491232v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1fd432forg.highwire.dtl.DTLVardef@45cdf0org.highwire.dtl.DTLVardef@d35b31org.highwire.dtl.DTLVardef@80702_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Following the footprints of variability during filopodia growth

Filopodia are actin-built finger-like dynamic structures that protrude from the cell cortex. These structures can sense the environment and play key roles in migration and cell-cell interactions. The growth-retraction cycle of filopodia is a complex process exquisitely regulated by intra- and extra-cellular cues, whose nature remains elusive. Filopodia present wide variation in length, lifetime and growth rate. Here, we investigate the features of filopodia patterns in fixed prostate cancer cells by confocal microscopy. Analysis of almost a thousand filopodia suggests the presence of two different populations: one characterized by a narrow distribution of lengths and the other with a much more variable pattern with very long filopodia. We explore a stochastic model of filopodia growth which takes into account diffusion and reactions involving actin and the regulatory proteins formin and capping, and retrograde flow. Interestingly, we found an inverse dependence between the filopodial length and the retrograde velocity. This result led us to propose that variations in the retrograde velocity could explain the experimental lengths observed for these tumor cells. In this sense, one population involves a wider range of retrograde velocities than the other population, and also includes low values of this velocity. It has been hypothesized that cells would be able to regulate retrograde flow as a mechanism to control filopodia length. Thus, we propound that the experimental filopodia pattern is the result of differential retrograde velocities originated from heterogeneous signaling due to cell-substrate interactions or prior cell-cell contacts.

biophysics↗

bioRxiv2019

Alternating regimes of motion in cell motility models

Cellular movement is a complex dynamic process, resulting from the interaction of multiple elements at the intra and extra-cellular levels. This epiphenomenon presents a variety of behaviors, which can include normal and anomalous diffusion or collective migration. In some cases cells can get neighborhood information through chemical or mechanical cues. A unified understanding about how such information can influence the dynamics of cell movement is still lacking. In order to improve our comprehension of cell migration we consider a cellular Potts model where cells move actively in the direction of a driving field. The intensity of this driving field is constant, while its orientation can evolves according to two alternative dynamics based on the Ornstein-Uhlenbeck process. In the first case, the next orientation of the driving field depends on the previous direction of the field. In the second case, the direction update considers the mean orientation performed by the cell in previous steps. Thus, the latter update rule mimics the ability of cells to perceive the environment, avoiding obstacles and thus increasing the cellular displacement. Our results indicate that both dynamics introduce temporal and spatial correlations in cell velocity in a friction coefficient and cell density dependent manner. Furthermore, we observe alternating regimes in the mean square displacement, with normal and anomalous diffusion. The crossovers between superdiffusive and diffusive regimes, are strongly affected by both the driving field dynamics and cell-cell interactions. In this sense, when cell polarization update grants information about the previous cellular displacement decreases the duration of the diffusive regime, in particular for high density cultures.

biophysics↗