Biology subjects

Famaey, N.

Publications and source records attributed to Famaey, N..

6 recordsLinked to original sources

bioRxiv2026

The Role of Smooth Muscle Cell Heterogeneity in Cerebral Autoregulation: A Multi-Scale Physics-Based Modeling Study

Background: Cerebral autoregulation stabilizes cerebral blood flow over a range of cerebral perfusion pressures, but the precise shape of the pressure-flow relationship remains debated. The classical triphasic pressure-flow relationship was recently challenged by experiments demonstrating a quadriphasic response, hypothesized to arise from vessel-size-dependent pressure-diameter responses. We tested this hypothesis and investigated whether these size-dependent responses originate from heterogeneity in smooth muscle cell (SMC) abundance, SMC behavior, or neither. Methods: We developed a computational multi-scale physics-based model of cerebral autoregulation linking SMC activity to vessel-scale diameter regulation and organ-scale blood flow. Four scenarios were evaluated: passive vessels, homogeneous SMC abundance and behavior, heterogeneous SMC abundance, and heterogeneous SMC behavior. Predicted pressure-diameter responses and pressure-flow relationships were compared across scenarios and against experimental observations. Results: In contrast to passive vessels, homogeneous SMC activation produced partial flow stabilization, highlighting the key role of SMCs in autoregulation. However, only heterogeneous SMC behavior reproduced the experimentally observed vessel-size-dependent trends in pressure-diameter responses. This scenario also showed the best agreement with the experimental organ-scale pressure-flow relationship (R-squared = 0.93, nRMSE = 5.96%). Conclusion: The model suggests that vessel-size-dependent SMC behavior underlies vessel-size-dependent pressure-diameter responses and shapes the relationship between cerebral perfusion pressure and cerebral blood flow.

bioengineering↗

bioRxiv2025

Community Challenge towards Consensus on Characterization of Biological Tissue: C4Bios First Findings

This study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress-stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research.

bioengineering↗

bioRxiv2025

Stretching the Limits: From Planar-Biaxial Stress-Stretch to Arterial Pressure-Diameter

Understanding the physiological condition of the vascular system is critical to explain, treat, and manage vascular disease. Numerous experimental and computational studies characterize the mechanical behavior of arterial tissue under controlled laboratory conditions. However, translating this knowledge into physiologically realistic conditions remains challenging. Key difficulties include selecting suitable and relevant test methods, minimizing uncertainty, and ensuring robust model validation. We present a novel integrative approach to translate laboratory experiments on arterial samples into clinically relevant pressure-diameter behavior. We perform controlled planar-biaxial tests on carotid arteries under three stretch ratios and generate axial and circumferential stress-stretch data to calibrate a fiber-reinforced soft tissue model. Using an analytical thick-walled cylindrical model, we predict subject-specific pressure-diameter behavior, informed by arterial prestretches from ring opening experiments. We systematically compare predictions against extension-inflation experiments on tubes from the same artery by applying controlled pairs of axial stretch and inner pressure, while recording outer diameter. We quantify prediction error in absolute and relative stretch regimes and evaluate the importance of the load-free reference dimensions. Results show how planar-biaxial tests probe different stretch regimes compared to extension-inflation deformations, leading to extrapolation of model predictions. We demonstrate how the constitutive material parameters can be fitted to different biomechanical loading conditions and assess the sensitivity of the simulations to axial stretch and circumferential prestretch. Only when key model parameters are accurately captured and their uncertainty propagated, planar-biaxial stress-stretch data can reliably predict arterial pressure-diameter behavior.

bioengineering↗

bioRxiv2025

Probing mycelium mechanics and taste: The moist and fibrous signature of fungi steak

Fungi-based meat is emerging as a promising class of nutritious, sustainable, and minimally processed biomaterials with the potential to complement or replace traditional animal and plant-based meats. However, its mechanical and sensory properties remain largely unknown. Here we characterize the quasi-static and dynamic mechanical behavior of fungi-based steak using multi-axial mechanical testing, rheology, and texture profile analysis. We find that the rate-independent response under quasi-static compression and shear is isotropic, while the rate-dependent response under dynamic compression is markedly anisotropic with stiffnesses and peak forces four times larger cross-plane than in-plane. Automated model discovery reveals that the exponential Demiray model best explains the rate-independent elastic response upon chewing in both directions. The rate-dependent directional stiffening can be linked, at least in part, to the high water content of mushroom root mycelium and to the restricted fluid flow at higher loading rates. Complementary sensory surveys reveal a strong correlation with mechanical metrics and suggest that we perceive the fungi-based steak as more moist, more viscous, and more fibrous than traditional animal- and plant-based meats. Taken together, our findings position fungi-based steak as an attractive, structurally equivalent, and sensorially compelling alternative protein source that is healthy for people and for the planet.

bioengineering↗

bioRxiv2024

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patients own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stressstrain relationships. Here we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks automatically discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.

bioengineering↗

bioRxiv2024

Mechanical Drivers of Glycosaminoglycan Content Changes in Intact and Damaged Human Cartilage

Articular cartilage undergoes significant degeneration during osteoarthritis, currently lacking effective treatments. This study explores mechanical influences on cartilage health using a novel finite element-based mechanoregulatory model, predicting combined degenerative and regenerative responses to mechanical loading. Calibrated and validated through one-week longitudinal ex vivo experiments on intact and damaged cartilage samples, the model underscores the roles of maximum shear strain, fluid velocity, and dissipated energy in driving changes in cartilage glycosaminoglycan (GAG) content. It delineates the distinct regenerative contributions of fluid velocity and dissipated energy, alongside the degenerative contribution of maximum shear strain, to GAG adaptation in both intact and damaged cartilage under physiological mechanical loading. Remarkably, the model predicts increased GAG production even in damaged cartilage, consistent with our in vitro experimental findings. Beyond advancing our understanding of mechanical loadings role in cartilage homeostasis, our model aligns with contemporary ambitions by exploring the potential of in silico trials to optimize mechanical loading in degenerative joint disease, fostering personalized rehabilitation.

bioengineering↗