Zohravi, E. | bioRxiv Science

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↗