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Biology subjects

Borau, C.

Publications and source records attributed to Borau, C..

5 recordsLinked to original sources

Cellfoundry: a GPU-accelerated, multi-physics ABM framework for cellular microenvironment and organoid-scale studies

Advanced in vitro systems such as organoids and microfluidic organ-on-a-chip platforms enable physiologically richer experimentation, but their complexity creates large parameter spaces and makes it difficult to disentangle the mechanistic roles of transport, mechanics, and extracellular microstructure. Agent-based modelling provides a natural computational counterpart to these systems by representing heterogeneous cells as discrete entities coupled through local rules and environmental fields. However, realistic microenvironment models often remain limited by scalability, simplified extracellular matrix representations, and the practical difficulty of calibrating large numbers of parameters. Here we present Cellfoundry, a computational framework built on a FLAMEGPU2-based modelling template for simulating complex cellular microenvironments. The framework integrates multiple interacting agent populations, including cells, fibrous networks, and focal adhesions mediating attachment dynamics and traction-force transmission. It combines mechanically resolved cell-cell and cell-matrix interactions with multi-species diffusion fields that propagate biochemical signals through the extracellular environment and regulate processes such as metabolism, migration, and cell-cycle progression. Cellfoundry also supports customizable behaviours across multiple cell types, enabling the study of heterogeneous multicellular systems within a unified computational setting. To support reproducible model development and calibration, the framework includes a fibre-network generation module, automated performance benchmarking workflows, post-processing and reporting utilities, and an Optuna-based Bayesian optimization pipeline with configurable single- and multi-objective targets. Two showcase examples illustrate these capabilities: a migration assay calibrated against fibroblast motility descriptors and a multi-objective organoid growth scenario reproducing target population composition and expansion dynamics and over time. Together, these examples demonstrate how Cellfoundry can be used to build, calibrate, and extend mechanistically interpretable models of coupled biochemical and mechanical dynamics in advanced in vitro systems. HighlightsO_LIHighly versatile, GPU-accelerated agent-based framework for cellular microenvironments C_LIO_LIExplicit fibrous ECM networks with dynamic remodelling and focal adhesion agents C_LIO_LICoupled mechanics and multi-species diffusion regulate cell behaviour in a highly customizable environment C_LIO_LIModular architecture with automated benchmarking and Bayesian parameter optimization C_LI

bioengineering↗

Regression-guided computational design of auxetic scaffolds for soft tissue applications

The mechanical performance of tissue-engineered scaffolds plays a critical role in their effectiveness for regenerative medicine applications. Auxetic metamaterials, characterized by a negative Poissons ratio, offer enhanced conformability and tunable mechanical behavior, making them promising candidates for scaffold design. This study presents a computational framework combining finite element method (FEM) simulations with regression-based predictive modeling to optimize auxetic scaffold architectures. A design of experiments (DOE) strategy enabled the training of FEM-accurate regression models capable of predicting mechanical responses from scaffold microstructural parameters. Sensitivity analysis guided the development of robust optimization strategies for identifying optimal geometries. Validation of the predictive framework was performed using experimentally derived, skin-representative mechanical properties from published literature, demonstrating strong agreement with FEM results. To facilitate broader use, we developed a software tool integrating this pipeline. It includes a manual mode for direct input of design and geometry parameters, and a predictive mode that returns optimized scaffold designs based on target properties. This integrated methodology supports robust, customizable scaffold design, advancing patient-specific approaches in soft tissue engineering.

bioengineering↗

Rational Design and Modeling of Auxetic Fiber Scaffolds for Soft Tissue Engineering via Melt Electrowriting

BackgroundThe development of regenerative biomaterial scaffolds with mechanical properties tailored to native soft tissues remains a challenge in tissue engineering. Mechanical metamaterials with auxetic architectures have taken enormous flight over the past years, as they offer improved control over mechanical function and performance through spatially defined material architecture. However, despite their potential, auxetic scaffold designs are often developed empirically, and the relationship between scaffold microarchitecture, mechanical function, and manufacturability remains poorly defined, limiting their applicability in tissue engineering. In this study, we present a computational framework for the rational design of auxetic fiber scaffolds manufactured using Melt Electro-writing (MEW) for soft tissue engineering. To address the challenge of tailoring scaffold mechanical properties to native tissue, we integrated finite element method (FEM) modeling with experimental validation to predict the mechanical behavior of the scaffolds. MethodThe micro-fibrous scaffolds with customizable auxetic re-entrant structures were designed using Python scripts for generating G-code used in fabrication and for model definition in FEM modeling, respectively. Four distinct auxetic structures - HCELL, SREG, SINV, and STRI - were selected for this study to cover the wide range of mechanical properties of soft tissue, and their geometry parameters were adjusted to reduce the pore size while considering printing resolution. FEM modeling was employed to predict the mechanical behavior of the scaffolds. To validate the predictions, the scaffolds were manufactured via MEW and characterized mechanically under biaxial tensile loading. Results and conclusionMEW manufacturing enabled the production of auxetic fiber scaffolds with sub-millimeter pore resolution, while rational design allowed to correct fabrication discrepancies and improve printed design fidelity. Experimental biaxial characterization of the scaffolds revealed design-dependent, biphasic, non-linear mechanical response, and the developed FEM model was capable of not only accurately simulating the observed mechanical response but also accounting for variations introduced by MEW during fabrication. Additionally, auxetic scaffolds supported fibroblast viability over 7 days, confirming biocompatibility as a first step toward soft tissue engineering. The good alignment between numerical predictions and experimental results highlights the value of FEM modeling as a powerful computational tool for guiding the design and optimization of auxetic scaffolds with tailored mechanical performance for specific soft tissue requirements. The presented methodology offers a significant advancement in the development of functional biomaterials for biomedical applications.

bioengineering↗

CONSERVED NUCLEAR MORPHOLOGY IDENTIFIES FUNCTIONAL RADIAL GLIA NEURAL PROGENITORS

Mechanical cues influence neural development, yet how tissue architecture is integrated into progenitor cell states remains poorly understood. Here, we show that defined microtopographies induce an early nuclear remodeling program associated with radial glia (RG)-like competence. Aligned microgrooves promote nuclear elongation, reduced Lamin A/C to B1 ratio, sustained {beta}-catenin activity, and distinct patterns of nuclear calcium dynamics, all of which merge before peak Pax6 expression. Pharmacological inhibition of mechanosensitive calcium signaling abolishes RG-associated marker induction while preserving nuclear remodeling, indicating that calcium-dependent pathways are required for Pax6 induction but are dispensable for the establishment of the underlying morphometric state. To quantitatively describe these transitions, we developed an interpretable model based on nuclear geometry and local cell density that identifies morphometric states associated with RG-like competence across experimental conditions. Application of this framework to embryonic mouse and human cortex revealed analogous signatures in native RG populations. Together, these findings indicate that tissue architecture influences RG-like competence through conserved nuclear morphometric states across developmental contexts.

developmental biology↗

Brain tissue mechanics is governed by microscale relations of the tissue constituents

Local mechanical tissue properties are a critical regulator of cell function in the central nervous system (CNS) during development and disorder. However, we still dont fully understand how the mechanical properties of individual tissue constituents, such as cell nuclei or myelin, determine tissue mechanics. Here we developed a model predicting local tissue mechanics, which induces non-affine deformations of the tissue components. Using the mouse hippocampus and cerebellum as model systems, we show that considering individual tissue components alone, as identified by immunohistochemistry, is not sufficient to reproduce the local mechanical properties of CNS tissue. Our results suggest that brain tissue shows a universal response to applied forces that depends not only on the amount and stiffness of the individual tissue constituents but also on the way how they assemble. Our model may unify current incongruences between the mechanics of soft biological tissues and the underlying constituents and facilitate the design of better biomedical materials and engineered tissues. To this end, we provide a freely-available platform to predict local tissue elasticity upon providing immunohistochemistry images and stiffness values for the constituents of the tissue.

biophysics↗