Biology subjects

Ellero, M.

Publications and source records attributed to Ellero, M..

4 recordsLinked to original sources

bioRxiv2024

Aggregation and disaggregation of red blood cells: depletion versus bridging

The aggregation of red blood cells (RBCs) is a complex phenomenon that strongly impacts blood flow and tissue perfusion. Despite extensive research for more than 50 years, physical mechanisms that govern RBC aggregation are still under debate. Two proposed mechanisms are based on bridging and depletion interactions between RBCs due to the presence of macromolecules in blood plasma. The bridging hypothesis assumes the formation of bonds between RBCs through adsorbing macromolecules, while the depletion mechanism results from the exclusion of macromolecules from the inter-cellular space, leading to effective attraction. Existing experimental studies generally cannot differentiate between these two aggregation mechanisms, though several recent investigations suggest concurrent involvement of the both mechanisms. We explore dynamic aggregation and disaggregation of two RBCs using three simulation models: a potential-based model mimicking depletion interactions, a bridging model with immobile bonds, and a new bridging model with mobile bonds which can slide along RBC membranes. Simulation results indicate that dynamic aggregation of RBCs primarily arises from depletion interactions, while disaggregation of RBCs involves both mechanisms. The bridging model with mobile bonds reproduces well the corresponding experimental data, offering insights into the interplay between bridging and depletion interactions and providing a framework for studying similar interactions between other biological cells.

biophysics↗

bioRxiv2024

Mesoscale Modelling of Fibrin Clots: The Interplay between Rheology and Microstructure at the Gel Point

This study presents a numerical model for incipient fibrin-clot formation that captures characteristic rheological and microstructural features of the clot at the gel point. Using a mesoscale-clustering framework, we evaluate the effect of gel concentration or gel volume fraction and branching on the fractal dimension, the gel time, and the viscoelastic properties of the clots. We show that variations in the gel concentration of our model can reproduce the effect of thrombin in the formation of fibrin clots. In particular, the model reproduces the fractal dimensions dependency on gel concentration and the trends in elasticity and gelation time with varying thrombin concentrations. This approach allows us to accurately recreate the gelation point of fibrin-thrombin gels, highlighting the intricate process of fibrin polymerization and gel network formation. This is critical for applications in the clinical and bioengineering fields where precise control over the gelation process is required.

biophysics↗

bioRxiv2023

Computational Mesoscale Framework for Biological Clustering and Fractal Aggregation

Complex hierarchical clustering mediated by diffusion and reaction is ubiquitous to many naturally occurring phenomena. The aggregates typically exhibit a fractal behavior or non-integer size scaling compared to their intrinsic dimensionality (2 - 3 dimensions). Such fractal aggregates have attracted attention in studying biological (i.e. bronchi and nervous system morphogenesis, blood clotting) and synthetic (i.e. colloids, polymers, catalysts, nano-dendrites, multicellular organisms) systems. In general, biological clustering can occur on a wide range of spatial/temporal scales, and depending on the type of interactions, multiple mechanisms (or stages) can be involved. As a consequence, the modeling of biological clustering is typically a challenging task, requiring the use of a variety of methods to capture the characteristic behavior of specific biological systems. Herein, we proposed a generalized-mesoscale-clustering (GMC) framework that incorporates hydrodynamic interactions, bonding, and surface tension effects. This framework allows for studying both static and dynamic states of cluster development. We showcase the framework using a variety of biological clustering mechanisms, and further illustrate its versatility to model different scales, focusing on blood-related clustering ranging from fibrin network formation to platelet aggregation. Besides the introduction of the mesoscale clustering framework, we show that a single biomarker (such as fractal dimension) is insufficient to fully characterize and distinguish different cluster structures (morphologies). To overcome this limitation, we propose a comprehensive characterization that relates the structural properties of the cluster using four key parameters, namely the fractal dimension, pore-scale diffusion, as well as the characteristic times for initiation and consolidation of the cluster. Additionally, we show that the GMC framework allows tracking of bond density providing another biomarker for cluster temporal evolution and final steady-state. Furthermore, this feature and built-in hydrodynamics interactions offer the potential to investigate cluster mechanical properties in a variety of biological systems.

biophysics↗

bioRxiv2022

Hydrodynamics of spike proteins dictate a transport-affinity competition for SARS-CoV-2 and other enveloped viruses

Many viruses, such as SARS-CoV-2 or Influenza, possess spike-decorated envelopes. Depending on the virus type, a large variability is present in spikes number, morphology and reactivity, which remains generally unexplained. Since viruses transmissibility depend on features beyond their genetic sequence, new tools are required to discern the effects of spikes functionality, interaction, and morphology. Here, we postulate the relevance of hydrodynamic interactions in the viral infectivity of enveloped viruses and propose micro-rheological characterization as a platform for viruses differentiation. To understand how the spikes affect virion mobility and infectivity, we investigate the diffusivity of spike-decorate structures using mesoscopic-hydrodynamic simulations. Furthermore, we explored the interplay between affinity and passive viral transport. Our results revealed that the diffusional mechanism of SARS-CoV-2 is strongly influenced by the size and distribution of its spikes. We propose and validate a universal mechanism to explain the link between optimal virion structure and maximal infectivity for many virus families.

biophysics↗