Biology subjects

St. Pierre, S. R.

Publications and source records attributed to St. Pierre, S. R..

12 recordsLinked to original sources

bioRxiv2026

Probabilistic model discovery reveals distinct constitutive behavior of kidney cortex and medulla

The kidney is a critical soft-tissue organ responsible for blood filtration. Accurate constitutive models of the kidney are essential to predict tissue deformation and stress, yet existing models prescribe the strain energy function a priori, largely treat tissue variability as deterministic, and do not distinguish between cortex and medulla. Here we use Gaussian constitutive artificial neural networks to discover probabilistic strain energy functions for kidney cortex and medulla from tension, compression, and shear experiments. The medulla is approximately twice as stiff as the cortex across all three modes: The effective Young's moduli are 3.41, 5.04, and 4.48 kPa for the medulla and 1.43, 2.32, and 2.87 kPa for the cortex in tension, compression, and shear. Both regions display pronounced tension--compression asymmetry. When trained on all three modes, the network discovers strain energy functions in the second invariant alone over the tested deformation range. The cortex model selects two exponential I2 terms, while the medulla model selects an additional linear I2 term. Gaussian external weights propagate specimen-to-specimen variability into closed-form probabilistic stress predictions. Our region-specific probabilistic models capture the mechanical heterogeneity and variability of the kidney and enable more realistic simulations of kidney deformation and stress for surgical planning, needle interventions, and renal trauma.

bioengineering↗

bioRxiv2026

The Meatball Matchup: Plant vs. Animal Proteins on Campus

Institutional dining halls increasingly serve plant-based meat products, yet it remains unclear how consumers perceive these products relative to animal-based options in real-world settings. This study compares consumer sensory perceptions of two plant-based meatballs (soy, soy-wheat) and two animal-based meatballs (beef, beef-mushroom) among university dining hall patrons (n = 116), complemented by instrumental Texture Profile Analysis. Animal-based meatballs received significantly higher ratings for moistness, meatiness, fattiness, and tastiness (all p < 0.001), with the meatiness gap being the largest ({Delta} = 1.40 on a 5-point scale). Texture analysis revealed that animal-based samples were significantly harder, more cohesive, and chewier than plant-based samples. In contrast, consumers perceived no differences in chewiness or hardness between categories, revealing a disconnect between instrumental and sensory measures. Just-About-Right penalty analysis identified insufficient savoriness as a universal improvement target across all products, including beef. Flavor and texture emerged as the dominant drivers of dining choice, while sustainability and animal welfare ranked lowest in importance. These findings suggest that sensory parity--particularly in moistness, meatiness, and savoriness--may drive acceptance of plant-based meat more strongly than sustainability messaging. Data and code are available at https://github.com/LivingMatterLab/AI4Food

bioengineering↗

bioRxiv2026

Mechanical, rheological, and sensory characterization of lion's mane mushroom steak

Mushrooms are increasingly recognized as delicious, nutritious, and sustainable foods, with an intrinsic umami flavor and fibrous microstructure that can approximate meat-like texture. Among them, lions mane mushroom has emerged as a promising candidate for whole-cut meat alternatives. Yet, its mechanical, rheological, and sensory properties remain largely unquantified. Here we show that a minimally processed lions mane mushroom steak exhibits distinctive mechanical, rheological, and sensory characteristics that position it favorably among existing meat alternatives. Despite its pronounced fibrous morphology, lions mane steak behaves predominantly as an isotropic material under both mechanical loading and rheological testing, with elastic stiffnesses of E = 33.2 kPa and E = 34.8 kPa in-plane and cross-plane. A fundamental challenge in alternative protein development is to understand how these measurable physical properties relate to human texture perception. In a complementary sensory survey, n = 21 participants ranked lions mane steak as more fatty, fibrous, moist, and meaty than eight animal- and plant-based comparison meats. Strikingly, our perceived sensory softness correlates inversely with our experimentally measured mechanical stiffness ({tau}=-0.60, p = 0.02) and rheological loss modulus ({tau}=-0.56, p = 0.03). Taken together, our results demonstrate that lions mane steak combines favorable mechanical performance with desirable sensory attributes and provide a mechanistic link between physics and taste. Our findings highlight lions mane mushroom as a compelling whole-cut alternative protein and underscore the value of integrated mechanical-sensory characterization for rational food design.

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↗

bioRxiv2025

Biaxial testing and sensory texture evaluation of plant-based and animal deli meat

Animal agriculture is one of the largest contributors to global carbon emissions. Plant-based meats offer a sustainable alternative to animal meat; yet, people are reluctant to switch their diets and spending habits, in large due to the taste and texture of plant-based meats. Deli meat is a convenient form of protein commonly used in sandwiches, yet little is known about its material or sensory properties. Here we performed biaxial testing with multiple different stretch ratios of four plant-based and four animal deli meats and fit accurate material models to the resulting stress-stretch data. Strikingly, the plant-based products, turkey, ham, deli, and prosciutto, with stiffnesses of 378 {+/-} 15 kPa, 343 {+/-} 62 kPa, 213 {+/-} 25 kPa, and 113 {+/-} 56 kPa, were more than twice as stiff as their animal counterparts, turkey, chicken, ham, and prosciutto, with 134 {+/-} 46 kPa, 117 {+/-} 17 kPa, 117 {+/-} 21 kPa, and 49 {+/-} 21 kPa. In a complementary sensory texture survey, n = 18 participants were able to correlate the physical stiffness with the sensory brittleness, with Spearmans correlation coefficient of{rho} = 0.857 and p = 0.011, but not with the sensory softness or hardness. Notably, the participants perceived all four plant-based products as less fibrous, less moist, and less meaty than the four animal products. Our study confirms the common belief that plant-based products struggle to meet the physical and sensory signature of animal deli meats. We anticipate that integrating rigorous mechanical testing, physics-based modeling, and sensory texture surveys could shape the path towards designing delicious, nutritious, and environmentally friendly meats that mimic the texture and mouthfeel of animal products and are healthy for people and for the planet. Data and code are freely available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2024

Texture profile analysis and rheology of plant-based and animal meat

Plant-based meat can help combat climate change and health risks associated with high meat consumption. To create adequate mimics of animal meats, plant-based meats must match in mouthfeel, taste, and texture. The gold standard to characterize the texture of meat is the double compression test, but this test suffers from a lack of standardization and reporting inconsistencies. Here we characterize the texture of five plant-based and three animal meats using texture profile analysis and rheology, and report ten mechanical features associated with each products elasticity, viscosity, and loss of integrity. Our findings suggest that, of all ten features, the stiffness, storage, and loss moduli are the most meaningful and consistent parameter to report, while other parameters suffer from a lack of interpretability and inconsistent definitions. We find that the sample stiffness varies by an order of magnitude, from 418.9{+/-}41.7kPa for plant-based turkey to 56.7{+/-}14.1kPa for tofu. Similarly, the storage and loss moduli vary from 50.4{+/-}4.1kPa and 25.3{+/-}3.0kPa for plant-based turkey to 5.7{+/-}0.5kPa and 1.3{+/-}0.1kPa for tofu. All three animal products, animal turkey, sausage, and hotdog, consistently rank in between these two extremes. Our results suggest that-with the right ingredients, additives, and formulation-modern food fabrication techniques can create plant-based meats that successfully replicate the full viscoelastic texture spectrum of processed animal meat.

bioengineering↗

bioRxiv2024

Mimicking mechanics: A comparison of meat and meat analogs

The texture of meat is one of the most important features to mimic when developing meat analogs. Both protein source and processing method impact the texture of the final product. We can distinguish three types of mechanical tests to quantify the textural differences between meat and meat analogs: puncture-type, rheological torsion tests, and classical mechanical tests of tension, compression, and bending. Here we compile the shear force and stiffness values of whole and comminuted meats and meat analogs from the two most popular tests for meat, the Warner-Bratzler shear test and the double-compression texture profile analysis. Our results suggest that, with the right fine-tuning, todays meat analogs are well capable of mimicking the mechanics of real meat. While Warner-Bratzler shear tests and texture profile analysis provide valuable information about the tenderness and sensory perception of meat, both tests suffer from a lack of standardization, which limits cross-study comparisons. Here we provide guidelines to standardize meat testing and report the meat stiffness as the single most informative mechanical parameter. Collecting big standardized data and sharing them with the community at large could empower researchers to harness the power of generative artificial intelligence to inform the systematic development of meat analogs with desired mechanical properties and functions, taste and sensory perception.

bioengineering↗

bioRxiv2024

Got meat? The mechanical signature of plant-based and animal meat

Eating less meat is associated with a healthier body and planet. Yet, we remain reluctant to switch to a plant-based diet, largely due to the sensory experience of plant-based meat. Food scientists characterize meat using a double compression test, which only probes one-dimensional behavior. Here we use tension, compression, and shear tests-combined with constitutive neural networks-to automatically discover the behavior of eight plant-based and animal meats across the entire three-dimensional spectrum. We find that plant-based sausage and hotdog, with stiffnesses of 95.9{+/-}14.1kPa and 38.7{+/-}3.0kPa, successfully mimic their animal counterparts, with 63.5{+/-}45.7kPa and 44.3{+/-}13.2 kPa, while tofurky is twice as stiff, and tofu is twice as soft. Strikingly, a complementary food tasting survey produces in nearly identical stiffness rankings for all eight products ({rho} =0.833, p=0.015). Probing the fully three-dimensional signature of meats is critical to understand subtle differences in texture that may result in a different perception of taste. Our data and code are freely available at https://github.com/LivingMatterLab/CANN

bioengineering↗

bioRxiv2023

Discovering the mechanics of artificial and real meat

Artificial meat is an eco-friendly alternative to real meat that is marketed to have a similar taste and feel. The mechanical properties of artificial meat significantly influence our perception of taste, but how precisely the mechanics of artificial meat compare to real meat remains insufficiently understood. Here we perform mechanical tension, compression, and shear tests on isotropic artificial meat (Tofurky(R) Plant-Based Deli Slices), anisotropic artificial meat (Daring Chickn Pieces) and anisotropic real meat (chicken) and analyze the data using constitutive neural networks and automated model discovery. Our study shows that, when deformed by 10%, artificial and real chicken display similar maximum stresses of 21.0 kPa and 21.8 kPa in tension, -7.2 kPa and -16.4 kPa in compression, and 2.4 kPa and 0.9 kPa in shear, while the maximum stresses for tofurky were 28.5 kPa, -38.3 kP, and 5.5 kPa. To discover the mechanics that best explain these data, we consulted two constitutive neural networks of Ogden and Valanis-Landel type. Both networks robustly discover models and parameters to explain the complex nonlinear behavior of artificial and real meat for individual tension, compression, and shear tests, and for all three tests combined. When constrained to the classical neo Hooke, Blatz Ko, and Mooney Rivlin models, both networks discover shear moduli of 94.4 kPa for tofurky, 35.7 kPa for artificial chickn, and 21.4 kPa for real chicken. Our results suggests that artificial chicken succeeds in re-producing the mechanical properties of real chicken across all loading modes, while tofurky does not, and is about three times stiffer. Strikingly, all three meat products display shear softening and their resistance to shear is about an order of magnitude lower than their resistance to tension and compression. We anticipate our study to inspire more quantitative, mechanistic comparisons of artificial and real meat. Our automated-model-discovery based approach has the potential to inform the design of more authentic meat substitutes with an improved perception of taste, with the ultimate goal to reduce environmental impact, improve animal welfare, and mitigate climate change, while still offering the familiar taste and texture of traditional meat. Our source code, data, and examples will be available at https://github.com/LivingMatterLab/CANNs.

bioengineering↗

bioRxiv2023

Principal-stretch-based constitutive neural networks autonomously discover a subclass of Ogden models for human brain tissue

The soft tissue of the brain deforms in response to external stimuli, which can lead to traumatic brain injury. Constitutive models relate the stress in the brain to its deformation and accurate constitutive modeling is critical in finite element simulations to estimate injury risk. Traditionally, researchers first choose a constitutive model and then fit the model parameters using tension, compression, or shear experiments. In contrast, constitutive artificial neural networks enable automated model discovery without having to choosing a specific model a priori before learning the model parameters. Here we reverse engineer a constitutive artificial neural network that uses the principal stretches, raised to a wide range of exponential powers, as activation functions for the hidden layer. Upon training, the network autonomously discovers a subclass of models with multiple Ogden terms that outperform popular constitutive models including neo Hooke, Blatz Ko, and Mooney Rivlin. While invariant-based networks fail to capture the pronounced tension-compression asymmetry of brain tissue, our principal-stretch-based network can simultaneously explain tension, compression, and shear data for the cortex, basal ganglia, corona radiata, and corpus callosum. Without fixing the number of terms a priori, our model self-selects the best subset of terms out of more than a million possible combinations, while simultaneously discovering the best model parameters and best experiment to train itself. Eliminating user-guided model selection has the potential to induce a paradigm shift in soft tissue modeling and democratize brain injury simulations. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

biophysics↗

bioRxiv2022

Automated model discovery for human brain using Constitutive Artificial Neural Networks

The brain is our softest and most vulnerable organ, and understanding its physics is a challenging but significant task. Massive efforts have been dedicated at testing the human brain, and various competing models have emerged to characterize its response to mechanical loading. However, selecting the best constitutive model remains a heuristic process that strongly depends on user experience and personal preference. Here we challenge the conventional wisdom to first select a constitutive model and then fit its parameters to experimental data. Instead, we propose a new strategy that simultaneously discovers both model and parameters that best describe the data. Towards this goal, we integrate more than a century of knowledge in thermodynamics and state-of-the-art machine learning to build a family of Constitutive Artificial Neural Networks that enable automated model discovery for human brain tissue. Our overall design paradigm is to reverse engineer a Constitutive Artificial Neural Network from a set of functional building blocks that are, by design, a generalization of widely used and commonly accepted constitutive models, including the neo Hooke, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models. By constraining the input, output, activation functions, and architecture, our network a priori satisfies thermodynamic consistency, material objectivity, material symmetry, physical constrains, and polyconvexity. We demonstrate that our network autonomously discovers both model and parameters that best characterize the behavior of human gray and white matter under tension, compression, and shear. Importantly, our network weights translate naturally into physically meaningful material parameters, e.g., shear moduli of 1.82kPa, 0.88kPa, 0.94kPa, and 0.54kPa for the cortex, basal ganglia, corona radiata, and corpus callosum. Our results suggest that Constitutive Artificial Neural Networks have the potential to induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2020

Condensation tendency of connected contractile tissue with planar isotropic actin network

ABSTRACTIt has been found that many types of cells form nematic symmetry on confined planar substrates. Such observation has been satisfactorily explained by modeling cells as crowded self-propelled rods. In this work, we report that rat embryonic fibroblast (REF) cells when confined in circular mesoscale patterns, form a new type of symmetry where cells align radially at the boundary. Unlike NIH-3T3 and MDCK monolayers, the REF monolayer presents a supracellular actin gradient with isotropic meshwork. In addition, the contractile REF cells present strong adhesive interactions with neighboring cells, which confers the monolayer with significant condensation tendency. We found the loss of condensation tendency by inhibiting the cell contractility or disrupting cell-cell adhesion led to the disappearance of the radial alignment. In theory, we found the prestretch due to condensation tendency with differential cell stiffness is sufficient to explain the new symmetry within a confined tissue continuum.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗