Biology subjects

Bordas, S. P. A.

Publications and source records attributed to Bordas, S. P. A..

5 recordsLinked to original sources

bioRxiv2024

Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism

This work introduces a machine learning framework that allows the investigation of the influence of reaction centers on the metabolic state of astrocyte cells. The proposed ML framework takes advantage of spatial astrocyte metabolic data stemming from numerical simulations for different reaction center configurations and allows for the following: (i) Discovery of cell groups of similar metabolic states and investigation of the reaction center configuration within each group. This approach allows for an analysis of the importance of the specific location of the reaction centers for a potentially critical metabolic state of the cell. (ii) Qualitative prediction of the energetic state of the cell (based on [ATP]: [ADP]) and quantitative prediction of the metabolic state of the cell by predicting the spatial average concentration of the metabolites or the complete spatial metabolic profile within the cell. (iii) Finally, the framework allows for the post hoc analysis of the developed quantitative predictive models using a SHAP approach to investigate the influence of the reaction center positions for further support of the insights drawn in steps (i)-(iii). Following the implementation of the framework, we observe that a uniform mitochondrial distribution within the cell results in the most robust energetic cell state. On the contrary, realizations of polarized mitochondrial distributions exhibit the worst overall cell health. Furthermore, we can make accurate qualitative predictions regarding cell health (accuracy = 0.9515, recall = 0.9753) and satisfactory predictions for the spatial average concentration and spatial concentration profiles of most of the metabolites involved. The techniques proposed in this study are not restricted to the dataset used. They can be easily used in other datasets that include findings from various metabolic computational models.

systems biology↗

bioRxiv2024

Mechanics of knee meniscus results from precise balance between material microstructure and synovial fluid viscosity

The meniscus plays a crucial role in the biomechanics of the knee, serving as load transmitter, and reducing friction between joints. Understanding the biome-chanics of the meniscus is essential to effective treatments of knee injuries and degenerative conditions. In this study, we used two central meniscus samples extracted from a human knee and acquired high-resolution {micro}-CT images. Using an implicit immersed boundary technique, we reconstructed two 3D computational models of the menisci. By eroding the channels of the original meniscus geometry, we created new microstructures with varying porosities (0.53 to 0.8) whilst preserving the connectivity of the porous structure. We investigate the fluid dynamics of the meniscus using a mesh-free numerical method, considering various inlet pressure conditions and analysing the fluid flow within the microstructures. The results of the original microstructure associated with a physiological dynamic viscosity of synovial fluid are in accordance with biophysical experiments on menisci. Furthermore, the eroded microstructure with a 33% increase in porosity exhibited a remarkable 120% increase in flow velocity. This emphasises the sensitivity of meniscus physiology to porous microstructure properties, showing that detailed computational models can explore physiological and pathological conditions, advancing further knee biomechanics research.

bioengineering↗

bioRxiv2024

Simple data-driven agent-based model of scratch assays on healthy and keloid fibroblasts

In this study we propose a novel agent-based model to reproduce and propose new hypotheses on the biological mechanisms of cell-cell interactions and cell migration from data obtained during scratch assay with healthy and keloid fibroblasts. The advantage of the agent-based model we propose in this paper lies in its simplicity: only three governing parameters. We conducted a parametric sensitivity analysis and we incorporated the evaluation of contact inhibition of locomotion, aligning with the observed loss during malignant invasion. To study invasion modalities, we conducted in vitro wound healing assays using healthy and pseudo-tumoral (keloid) fibroblasts under diverse conditions: control, macrophage type 1 secretome, and macrophage type 2 secretome. Mitomycin inhibition of proliferation isolated the contribution of migration to wound filling. Our agent-based mathematical model describes configurations based on our microscopy imaging and statistical data, which enables quantitative comparisons between our experimental and numerical results. Calibration and evaluation were performed on the same experiments, enriched by external datasets. With only three governing parameters, our model not only demonstrated good agreement (8.78% to 18.75% error) with external evaluation datasets for all experimental configurations but also provided us with a nuanced understanding of keloid fibroblast behavior during wound healing, especially regarding contact inhibition dynamics.

systems biology↗

bioRxiv2022

Mechanistic multiscale modelling of energy metabolism in human astrocytes indicates morphological effects in Alzheimer's Disease

Astrocytes with their specialized morphology are essential for brain homeostasis as metabolic mediators between blood vessels and neurons. In neurodegenerative diseases such as Alzheimers disease (AD), astrocytes adopt reactive profiles with molecular and morphological changes that could lead to the impairment of their metabolic support and impact disease progres-sion. However, the underlying mechanisms how metabolic function of human astrocytes is impaired by their morphological changes in AD is still elusive. To address this challenge, we developed and applied a metabolic multiscale modelling approach integrating the dynamics of metabolic energy pathways and physiological astrocyte morphologies acquired in human AD and age-matched control brain samples. The results demonstrate that the complex cell shape and intracellular organization of energetic pathways determine the metabolic profile and support capacity of astrocytes in health and AD conditions. Thus, our mechanistic approach indicates the importance of spatial orchestration in metabolism and allows for the identification of protective mechanisms against disease-associated metabolic impairments.

systems biology↗

bioRxiv2020

Quantifying the role of mechanics in the free and encapsulated growth of cancer spheroids

Spheroids encapsulated within alginate capsules are emerging as suitable in vitro tools to investigate the impact of mechanical forces on tumor growth since the internal tumor pressure can be retrieved from the deformation of the capsule. Here we focus on the particular case of Cellular Capsule Technology (CCT). We show in this contribution that a modeling approach accounting for the triphasic nature of the spheroid (extracellular matrix, tumor cells and interstitial fluid) offers a new perspective of analysis revealing that the pressure retrieved experimentally cannot be interpreted as a direct picture of the pressure sustained by the tumor cells and, as such, cannot therefore be used to quantify the critical pressure which induces stress-induced phenotype switch in tumor cells. The proposed multiphase reactive poro-mechanical model was cross-validated. Parameter sensitivity analyses on the digital twin revealed that the main parameters determining the encapsulated growth configuration are different from those driving growth in free condition, confirming that radically different phenomena are at play. Results reported in this contribution support the idea that multiphase reactive poro-mechanics is an exceptional theoretical framework to attain an in-depth understanding of CCT experiments, to confirm their hypotheses and to further improve their design.

biophysics↗