https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Fri, 05 Jun 2026 23:13:22 GMT 2026-06-05T23:13:22Z http://hdl.handle.net/10985/25443 Single and bi-compartment poro-elastic model of perfused biological soft tissues: FEniCSx implementation and tutorial LAVIGNE, Thomas; URCUN, Stéphane; ROHAN, Pierre-Yves; SCIUME, Giuseppe; BAROLI, Davide; BORDAS, Stéphane Pierre Alain Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid–solid interactions. The time-dependent mechanical properties of soft tissues influence their physiological functions and are related to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multiphase flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted. This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference No. 17013182. Mon, 01 May 2023 00:00:00 GMT http://hdl.handle.net/10985/25443 2023-05-01T00:00:00Z LAVIGNE, Thomas URCUN, Stéphane ROHAN, Pierre-Yves SCIUME, Giuseppe BAROLI, Davide BORDAS, Stéphane Pierre Alain Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid–solid interactions. The time-dependent mechanical properties of soft tissues influence their physiological functions and are related to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multiphase flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted. http://hdl.handle.net/10985/25442 In vivo mechanical response of thigh soft tissues under compression: a two-layer model allows an improved representation of the local tissue kinematics SEGAIN, Alexandre; SCIUME, Giuseppe; PILLET, Helene; ROHAN, Pierre-Yves Biomechanical parameters have the potential to be used as physical markers for prevention and diagnosis. Finite Element Analysis (FEA) is a widely used tool to evaluate these parameters in vivo. However, the development of clinically relevant FEA requires personalisation of the geometry, boundary conditions, and constitutive parameters. This contribution focuses on the characterisation of mechanical properties in vivo which remains a significant challenge for the community. The aim of this retrospective study is to evaluate the sensitivity of the computed elastic parameters (shear modulus of fat and muscle tissues) derived by inverse analysis as a function of the geometrical modelling assumption (homogenised monolayer vs bilayer) and the formulation of the cost function. The methodology presented here proposes to extract the experimental force-displacement response for each tissue layer (muscle and fat) and construct the associated Finite Element Model for each volunteer, based on data previously collected in our group (N=7 volunteers) as reported in (Fougeron et al., 2020). The sensitivity analysis indicates that the choice of the cost function has minimal impact on the topology of the response surface in the parametric space. Each surface displays a valley of parameters that minimises the cost function. The constitutive properties of the thigh (reported as median ± interquartile range) were determined to be (μ=198±322 kPa,α=37) for the monolayer and (μ_(muscle )=1675±1127 kPa,α_muscle=22±14,μ_fat=537±1131 kPa,α_fat=32±7) for the bilayer. A comparison of the homogenised monolayer and bilayer models showed that adding a layer reduces the error on the local force displacement curves, increasing the accuracy of the local kinematics of soft tissues during indentation. This allows for an increased understanding of load transmission in soft tissue. The comparison of the two models in terms of strains indicates that the modelling choice significantly influences the localization of maximal compressive strains. These results support the idea that the biomechanical community should conduct further work to develop reliable methodologies for estimating in vivo strain in soft tissue. Wed, 01 May 2024 00:00:00 GMT http://hdl.handle.net/10985/25442 2024-05-01T00:00:00Z SEGAIN, Alexandre SCIUME, Giuseppe PILLET, Helene ROHAN, Pierre-Yves Biomechanical parameters have the potential to be used as physical markers for prevention and diagnosis. Finite Element Analysis (FEA) is a widely used tool to evaluate these parameters in vivo. However, the development of clinically relevant FEA requires personalisation of the geometry, boundary conditions, and constitutive parameters. This contribution focuses on the characterisation of mechanical properties in vivo which remains a significant challenge for the community. The aim of this retrospective study is to evaluate the sensitivity of the computed elastic parameters (shear modulus of fat and muscle tissues) derived by inverse analysis as a function of the geometrical modelling assumption (homogenised monolayer vs bilayer) and the formulation of the cost function. The methodology presented here proposes to extract the experimental force-displacement response for each tissue layer (muscle and fat) and construct the associated Finite Element Model for each volunteer, based on data previously collected in our group (N=7 volunteers) as reported in (Fougeron et al., 2020). The sensitivity analysis indicates that the choice of the cost function has minimal impact on the topology of the response surface in the parametric space. Each surface displays a valley of parameters that minimises the cost function. The constitutive properties of the thigh (reported as median ± interquartile range) were determined to be (μ=198±322 kPa,α=37) for the monolayer and (μ_(muscle )=1675±1127 kPa,α_muscle=22±14,μ_fat=537±1131 kPa,α_fat=32±7) for the bilayer. A comparison of the homogenised monolayer and bilayer models showed that adding a layer reduces the error on the local force displacement curves, increasing the accuracy of the local kinematics of soft tissues during indentation. This allows for an increased understanding of load transmission in soft tissue. The comparison of the two models in terms of strains indicates that the modelling choice significantly influences the localization of maximal compressive strains. These results support the idea that the biomechanical community should conduct further work to develop reliable methodologies for estimating in vivo strain in soft tissue. http://hdl.handle.net/10985/25444 Non-operable glioblastoma: Proposition of patient-specific forecasting by image-informed poromechanical model URCUN, Stéphane; BAROLI, Davide; ROHAN, Pierre-Yves; SKALLI, Wafa; LUBRANO, Vincent; BORDAS, Stéphane Pierre Alain; SCIUME, Giuseppe We propose a novel image-informed glioblastoma mathematical model within a reactive multiphase poromechanical framework. Poromechanics offers to model in a coupled manner the interplay between tissue deformation and pressure-driven fluid flows, these phenomena existing simultaneously in cancer disease. The model also relies on two mechano-biological hypotheses responsible for the heterogeneity of the GBM: hypoxia signaling cascade and interaction between extra-cellular matrix and tumor cells. The model belongs to the category of patient-specific image-informed models as it is initialized, calibrated and evaluated by the means of patient imaging data. The model is calibrated with patient data after 6 cycles of concomitant radiotherapy chemotherapy and shows good agreement with treatment response 3 months after chemotherapy maintenance. Sensitivity of the solution to parameters and to boundary conditions is provided. As this work is only a first step of the inclusion of poromechanical framework in image-informed glioblastoma mathematical models, leads of improvement are provided in the conclusion. Statement of significance: In this study, we employ mechanics of reactive porous media to effectively model the dynamic progression of a glioblastoma. Traditionally, glioblastoma tumors are surgically removed a few weeks post-diagnosis. To address this, we focus on a non-operable clinical scenario which allows us to have sufficient time points for the calibration and subsequent validation of our mathematical model. It is paramount to underscore that the tumor’s evolution is significantly influenced by chemotherapy and radiotherapy. These therapeutic effects find incorporation within our mathematical framework. Notably, the approach we present is distinctive for two key reasons: Firstly, the mathematical model inherently captures the complex multiphase and hierarchical nature of brain tissue. Secondly, our constitutive laws factor in the ever-changing properties of cells and tissues, mirroring the local phenotypic alterations observed within the tumor. This work constitutes an initial stride towards systematically integrating multiphase poromechanics into patient-specific glioblastoma growth modeling. As we look ahead, we acknowledge areas for potential enhancement in pursuit of advancing this promising direction. Work funding with a grant fromLuxembourg National Research Fund (FNR) grant number INTER/ANR/21/16399490 and from Réseau Santé des Arts et Métiers. Wed, 01 Mar 2023 00:00:00 GMT http://hdl.handle.net/10985/25444 2023-03-01T00:00:00Z URCUN, Stéphane BAROLI, Davide ROHAN, Pierre-Yves SKALLI, Wafa LUBRANO, Vincent BORDAS, Stéphane Pierre Alain SCIUME, Giuseppe We propose a novel image-informed glioblastoma mathematical model within a reactive multiphase poromechanical framework. Poromechanics offers to model in a coupled manner the interplay between tissue deformation and pressure-driven fluid flows, these phenomena existing simultaneously in cancer disease. The model also relies on two mechano-biological hypotheses responsible for the heterogeneity of the GBM: hypoxia signaling cascade and interaction between extra-cellular matrix and tumor cells. The model belongs to the category of patient-specific image-informed models as it is initialized, calibrated and evaluated by the means of patient imaging data. The model is calibrated with patient data after 6 cycles of concomitant radiotherapy chemotherapy and shows good agreement with treatment response 3 months after chemotherapy maintenance. Sensitivity of the solution to parameters and to boundary conditions is provided. As this work is only a first step of the inclusion of poromechanical framework in image-informed glioblastoma mathematical models, leads of improvement are provided in the conclusion. Statement of significance: In this study, we employ mechanics of reactive porous media to effectively model the dynamic progression of a glioblastoma. Traditionally, glioblastoma tumors are surgically removed a few weeks post-diagnosis. To address this, we focus on a non-operable clinical scenario which allows us to have sufficient time points for the calibration and subsequent validation of our mathematical model. It is paramount to underscore that the tumor’s evolution is significantly influenced by chemotherapy and radiotherapy. These therapeutic effects find incorporation within our mathematical framework. Notably, the approach we present is distinctive for two key reasons: Firstly, the mathematical model inherently captures the complex multiphase and hierarchical nature of brain tissue. Secondly, our constitutive laws factor in the ever-changing properties of cells and tissues, mirroring the local phenotypic alterations observed within the tumor. This work constitutes an initial stride towards systematically integrating multiphase poromechanics into patient-specific glioblastoma growth modeling. As we look ahead, we acknowledge areas for potential enhancement in pursuit of advancing this promising direction. http://hdl.handle.net/10985/25521 Oncology and mechanics: Landmark studies and promising clinical applications URCUN, Stéphane; LORENZO, Guillermo; BAROLI, Davide; ROHAN, Pierre-Yves; SCIUME, Giuseppe; SKALLI, Wafa; LUBRANO, Vincent; BORDAS, Stéphane Pierre Alain Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design, and therapeutic targets. Additionally, a specific focus is placed on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling. Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/10985/25521 2022-06-01T00:00:00Z URCUN, Stéphane LORENZO, Guillermo BAROLI, Davide ROHAN, Pierre-Yves SCIUME, Giuseppe SKALLI, Wafa LUBRANO, Vincent BORDAS, Stéphane Pierre Alain Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design, and therapeutic targets. Additionally, a specific focus is placed on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling. http://hdl.handle.net/10985/25648 Rapid Biofabrication of an Advanced Microphysiological System Mimicking Phenotypical Heterogeneity and Drug Resistance in Glioblastoma PUN, Sirjana; PRAKASH, Anusha; DEMAREE, Dalee; KRUMMEL, Daniel Pomeranz; SCIUME, Giuseppe; SENGUPTA, Soma; BARRILE, Riccardo AbstractMicrophysiological systems (MPSs) reconstitute tissue interfaces and organ functions, presenting a promising alternative to animal models in drug development. However, traditional materials like polydimethylsiloxane (PDMS) often interfere by absorbing hydrophobic molecules, affecting drug testing accuracy. Additive manufacturing, including 3D bioprinting, offers viable solutions. GlioFlow3D, a novel microfluidic platform combining extrusion bioprinting and stereolithography (SLA) is introduced. GlioFlow3D integrates primary human cells and glioblastoma (GBM) lines in hydrogel‐based microchannels mimicking vasculature, within an SLA resin framework using cost‐effective materials. The study introduces a robust protocol to mitigate SLA resin cytotoxicity. Compared to PDMS, GlioFlow3D demonstrated lower small molecule absorption, which is relevant for accurate testing of small molecules like Temozolomide (TMZ). Computational modeling is used to optimize a pumpless setup simulating interstitial fluid flow dynamics in tissues. Co‐culturing GBM with brain endothelial cells in GlioFlow3D showed enhanced CD133 expression and TMZ resistance near vascular interfaces, highlighting spatial drug resistance mechanisms. This PDMS‐free platform promises advanced drug testing, improving preclinical research and personalized therapy by elucidating complex GBM drug resistance mechanisms influenced by the tissue microenvironment. Mon, 05 Aug 2024 00:00:00 GMT http://hdl.handle.net/10985/25648 2024-08-05T00:00:00Z PUN, Sirjana PRAKASH, Anusha DEMAREE, Dalee KRUMMEL, Daniel Pomeranz SCIUME, Giuseppe SENGUPTA, Soma BARRILE, Riccardo AbstractMicrophysiological systems (MPSs) reconstitute tissue interfaces and organ functions, presenting a promising alternative to animal models in drug development. However, traditional materials like polydimethylsiloxane (PDMS) often interfere by absorbing hydrophobic molecules, affecting drug testing accuracy. Additive manufacturing, including 3D bioprinting, offers viable solutions. GlioFlow3D, a novel microfluidic platform combining extrusion bioprinting and stereolithography (SLA) is introduced. GlioFlow3D integrates primary human cells and glioblastoma (GBM) lines in hydrogel‐based microchannels mimicking vasculature, within an SLA resin framework using cost‐effective materials. The study introduces a robust protocol to mitigate SLA resin cytotoxicity. Compared to PDMS, GlioFlow3D demonstrated lower small molecule absorption, which is relevant for accurate testing of small molecules like Temozolomide (TMZ). Computational modeling is used to optimize a pumpless setup simulating interstitial fluid flow dynamics in tissues. Co‐culturing GBM with brain endothelial cells in GlioFlow3D showed enhanced CD133 expression and TMZ resistance near vascular interfaces, highlighting spatial drug resistance mechanisms. This PDMS‐free platform promises advanced drug testing, improving preclinical research and personalized therapy by elucidating complex GBM drug resistance mechanisms influenced by the tissue microenvironment.