Numerical modelling of the growth and remodelling phenomena in biological tissues

Living biological tissues are complex structures that have the capacity of evolving in response to external loads and environmental stimuli. The adequate modelling of soft biological tissue behaviour is a key issue in successfully reproducing biomechanical problems through computational analysis. This study presents a general constitutive formulation capable of representing the behaviour of these tissues through finite element simulation. It is based on phenomenological models that, used in combination with the generalized mixing theory, can numerically reproduce a wide range of material behaviours. First, the passive behaviour of tissues is characterized by means of hyperelastic and finite-strain damage models. A new generalized damage model is proposed, providing a flexible and versatile formulation that can reproduce a wide range of tissue behaviour. It can be particularized to any hyperelastic model and requires identifying only two material parameters. Then, the use of these constitutive models with generalized mixing theory in a finite-strain framework is described and tools to account for the anisotropic behaviour of tissues are put forth. The active behaviour of tissues is characterized through constitutive models capable of reproducing the growth and remodelling phenomena. These are built on the hyperelastic and damage formulations described above and, thus, represent the active extension of the passive tissue behaviour. A growth model considering biological availability is used and extended to include directional growth. In addition, a novel constitutive model for homeostatic-driven turnover remodelling is presented and discussed. This model captures the stiffness recovery that occurs in healing tissues, understood as a recovery or reversal of damage in the tissue, which is driven by both mechanical and biochemical stimuli. Finally, the issue of correctly identifying the material parameters for computational modelling is addressed. An inverse method using optimization techniques is developed to facilitate the identification of these parameters.Els teixits biològics vius són estructures complexes que tenen la capacitat d'evolucionar en resposta a càrregues externes i estímuls ambientals. El modelat adequat del comportament del teixit biològic tou és un tema clau per poder reproduir amb èxit problemes biomecànics mitjançant anàlisi computacional. Aquest estudi presenta una formulació constitutiva general capaç de representar el comportament d'aquests teixits mitjançant la simulació amb elements finits. Es basa en models fenomenològics que, usats en combinació amb la teoria de mescles generalitzada, permeten reproduir numèricament un ampli ventall de comportaments materials. Primer, el comportament passiu dels teixits es caracteritza amb models hiperelàstics i de dany en grans deformacions. Es proposa un model generalitzat de dany que proporciona una formulació versàtil i flexible per poder reproduir una extensa gamma de conductes de teixits. Pot ser particularitzat amb qualsevol model hiperelàstic i requereix identificar tan sols dos paràmetres materials. Llavors, es descriu l'ús d'aquests models constitutius en conjunt amb la teoria generalitzada de mescles, desenvolupada en el marc de grans deformacions, i es presenten eines que permeten incorporar les propietats anisòtropes dels teixits. El comportament actiu dels teixits es caracteritza mitjançant models constitutius capaços de reproduir els fenòmens de creixement i remodelació. Aquests es construeixen sobre les formulacions d'hiperelasticitat i dany descrites anteriorment i, per tant, suposen l'extensió activa del comportament passiu del teixit. Es fa servir un model de creixement que té en compte la disponibilitat biològica de l'organisme, que després s'amplia per incloure dany direccional en el model. També es presenta i analitza un nou model constitutiu per al remodelat per renovació tendint a l’homeòstasi (homeostatic-driven turnover remodelling). Aquest model captura la recuperació de rigidesa que s'observa en teixits que es guareixen. Aquí, el remodelat s'entén com la recuperació o inversió del dany en el teixit i és motivat tant per estímuls mecànics com bioquímics. Finalment, s'aborda el tema de la identificació correcta dels paràmetres materials per al modelat computacional. Es desenvolupa un mètode invers que fa ús de tècniques d'optimització per facilitar la identificació d'aquests paràmetre

The role of computational models in mechanobiology of growing bone

Endochondral ossification, the process by which long bones grow in length, is regulated by mechanical forces. Computational models, specifically finite element models, have been used for decades to understand the role of mechanical loading on endochondral ossification. This perspective outlines the stages of model development in which models are used to: 1) explore phenomena, 2) explain pathologies, 3) predict clinical outcomes, and 4) design therapies. As the models progress through the stages, they increase in specificity and biofidelity. We give specific examples of models of endochondral ossification and expect models of other mechanobiological systems to follow similar development stages.Peer ReviewedPostprint (published version

I’m stuck! How to efficiently debug computational solid mechanics models so you can enjoy the beauty of simulations

A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is hard to articulate as it relies mostly on the mental catalogues we have built with the experience of past failures. To help newcomers to the field of material modelling, here we attempt to fill this gap and provide a perspective on how to identify and fix mistakes in computational solid mechanics models. To this aim, we describe the components that make up such a model and then identify possible sources of errors. In practice, finding mistakes is often better done by considering the symptoms of what is going wrong. As a consequence, we provide strategies to narrow down where in the model the problem may be, based on observation and a catalogue of frequent causes of observed errors. In a final section, we also discuss how one-time bug-free models can be kept bug-free in view of the fact that computational models are typically under continual development. We hope that this collection of approaches and suggestions serves as a “road map” to find and fix mistakes in computational models, and more importantly, keep the problems solved so that modellers can enjoy the beauty of material modelling and simulation.EC and JPP wish to thank their former supervisor Paul Steinmann for the inspiration to write this paper, which can be traced back to the talk we prepared for the ECCM-ECFD conference held in Glasgow in 2018. EC’s work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 841047. WB’s work was partially supported by the National Science Foundation under award OAC-1835673; by award DMS-1821210; by award EAR-1925595; and by the Computational Infrastructure in Geodynamics initiative (CIG), through the National Science Foundation under Award EAR-1550901 and The University of California – Davis .Peer ReviewedPostprint (published version

Optimization method for the determination of material parameters in damaged composite structures

An optimization method to identify the material parameters of composite structures using an inverse method is proposed. This methodology compares experimental results with their numerical reproduction using the finite element method in order to obtain an estimation of the error between the results. This error estimation is then used by an evolutionary optimizer to determine, in an iterative process, the value of the material parameters which result in the best numerical fit. The novelty of the method is in the coupling between the simple genetic algorithm and the mixing theory used to numerically reproduce the composite behavior. The methodology proposed has been validated through a simple example which illustrates the exploitability of the method in relation to the modeling of damaged composite structures.Peer ReviewedPostprint (author’s final draft

A homeostatic-driven turnover remodelling constitutive model for healing in soft tissues

Remodelling of soft biological tissue is characterized by interacting biochemical and biomechanical events, which change the tissue's microstructure, and, consequently, its macroscopic mechanical properties. Remodelling is a well-defined stage of the healing process, and aims at recovering or repairing the injured extracellular matrix. Like other physiological processes, remodelling is thought to be driven by homeostasis, i.e. it tends to re-establish the properties of the uninjured tissue. However, homeostasis may never be reached, such that remodelling may also appear as a continuous pathological transformation of diseased tissues during aneurysm expansion, for example. A simple constitutive model for soft biological tissues that regards remodelling as homeostatic-driven turnover is developed. Specifically, the recoverable effective tissue damage, whose rate is the sum of a mechanical damage rate and a healing rate, serves as a scalar internal thermodynamic variable. In order to integrate the biochemical and biomechanical aspects of remodelling, the healing rate is, on the one hand, driven by mechanical stimuli, but, on the other hand, subjected to simple metabolic constraints. The proposed model is formulated in accordance with continuum damage mechanics within an open-system thermodynamics framework. The numerical implementation in an in-house finite-element code is described, particularized for Ogden hyperelasticity. Numerical examples illustrate the basic constitutive characteristics of the model and demonstrate its potential in representing aspects of remodelling of soft tissues. Simulation results are verified for their plausibility, but also validated against reported experimental data.Peer ReviewedPostprint (author's final draft

Local mechanical stimuli correlate with tissue growth in axolotl salamander joint morphogenesis

Movement-induced forces are critical to correct joint formation, but it is unclear how cells sense and respond to these mechanical cues. To study the role of mechanical stimuli in the shaping of the joint, we combined experiments on regenerating axolotl (Ambystoma mexicanum) forelimbs with a poroelastic model of bone rudiment growth. Animals either regrew forelimbs normally (control) or were injected with a transient receptor potential vanilloid 4 (TRPV4) agonist during joint morphogenesis. We quantified growth and shape in regrown humeri from whole-mount light sheet fluorescence images of the regenerated limbs. Results revealed significant differences in morphology and cell proliferation between groups, indicating that TRPV4 desensitization has an effect on joint shape. To link TRPV4 desensitization with impaired mechanosensitivity, we developed a finite element model of a regenerating humerus. Local tissue growth was the sum of a biological contribution proportional to chondrocyte density, which was constant, and a mechanical contribution proportional to fluid pressure. Computational predictions of growth agreed with experimental outcomes of joint shape, suggesting that interstitial pressure driven from cyclic mechanical stimuli promotes local tissue growth. Predictive computational models informed by experimental findings allow us to explore potential physical mechanisms involved in tissue growth to advance our understanding of the mechanobiology of joint morphogenesis.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 841047 and the National Science Foundation under grant no. 1727518. J.J.M. has been also funded by the Spanish Ministry of Science and Innovation under grant no. DPI2016-74929-R, and by the local government Generalitat de Catalunya under grant no. 2017 SGR 1278. K.L. was supported by a Northeastern University Undergraduate Research and Fellowships PEAK Experiences Award.Peer ReviewedPostprint (published version

Certification Calculations of the Torsion Box of an Aircraft’s Horizontal Tail-Plane

El TFC consisteix en la realització de la justificació estructural del calaix de torsió de l'estabilitzador horitzontal d'una aeronau. Les principals activitats a realitzar en el TFC són: 1.- definició/recopilació dels admissibles del materials 2.- selecció dels casos crítics de càrrega 3.- anàlisi detallat dels elements estructurals (spars, skins, stringers, stiffeners, etc.) 4.- compilació dels resultats. Els principals anàlisis estructurals que seràn duts a terme són: 1.- pandeig i resistència dels panells amb i sense forat 2.- interaccions entre pandeig i concentració d'esforços en panells amb forats 3.- pandeig, resistència i crippling dels rigiditzadors 4.- estudi del rematxat i pegat i dels angulars entre d'altres

A generalized finite-strain damage model for quasi-incompressible hyperelasticity using hybrid formulation

This is the accepted version of the following article: [Comellas, E., Bellomo, F. J., and Oller, S. (2015) A generalized finite-strain damage model for quasi-incompressible hyperelasticity using hybrid formulation. Int. J. Numer. Meth. Engng, doi: 10.1002/nme.5118.], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/nme.5118/abstractA new generalized damage model for quasi-incompressible hyperelasticity in a total Lagrangian finite-strain framework is presented. A Kachanov-like reduction factor (1 - D) is applied on the deviatoric part of the hyperelastic constitutive model. Linear and exponential softening are defined as damage evolution laws, both describable in terms of only two material parameters. The model is formulated following continuum damage mechanics theory such that it can be particularized for any hyperelastic model based on the volumetric–isochoric split of the Helmholtz free energy. However, in the present work, it has been implemented in an in-house finite element code for neo-Hooke and Ogden hyperelasticity. The details of the hybrid formulation used are also described. A couple of three-dimensional examples are presented to illustrate the main characteristics of the damage model. The results obtained reproduce a wide range of softening behaviors, highlighting the versatility of the formulation proposed. The damage formulation has been developed to be used in conjunction with mixing theory in order to model the behavior of fibered biological tissues. As an example, the markedly different behaviors of the fundamental components of the rectus sheath were reproduced using the damage model, obtaining excellent correlation with the experimental results from literature.Peer Reviewe

Modeling the porous and viscous responses of human brain tissue behavior

The biomechanical characterization of human brain tissue and the development of appropriate mechanical models is crucial to provide realistic computational predictions that can assist personalized treatment of neurological disorders with a strong biomechanical component. Here, we present a novel material model that combines finite viscoelasticity with a nonlinear biphasic poroelastic formulation, developed within the context of the Theory of Porous Media. Embedded in a finite element framework, our model is capable of predicting the brain tissue response under multiple loading conditions. We show that our model can capture both experimentally observed fluid flow and conditioning aspects of brain tissue behavior in addition to its well-established nonlinear and compression–tension asymmetric characteristics. Our results support the notion that porous and viscous effects are highly interrelated and that additional experimental data are required to reliably identify the model parameters. The modular and object-oriented design with automatic differentiation makes our open-source code easily amendable to future extensions. We provide a solid foundation towards the development of a reliable and comprehensive biomechanical model for brain tissue, which will be a versatile and useful tool in elucidating the rheology of brain tissue behavior to help the biomedical and clinical communities in the future study, prevention and treatment of brain injury and diseasePeer ReviewedPostprint (author's final draft

Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose

Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time. For those unfamiliar with machine learning or 3D image analysis, the rapid advancement of the field can make navigating the newest software options confusing. In this paper, two open-source machine learning algorithms, Cellpose and Stardist, are compared in their application on a 3D light sheet dataset counting fluorescently stained proliferative cell nuclei. The effects of image tiling and background subtraction are shown through image analysis pipelines for both algorithms. Based on our analysis, the relative ease of use of Cellpose and the absence of need to train a model leaves it a strong option for 3D cell segmentation despite relatively longer processing times. When Cellpose's pretrained model yields results that are not of sufficient quality, or the analysis of a large dataset is required, Stardist may be more appropriate. Despite the time it takes to train the model, Stardist can create a model specialized to the users' dataset that can be iteratively improved until predictions are satisfactory with far lower processing time relative to other methods.This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 841047 and NSF CMMI #1727518.Peer ReviewedPostprint (author's final draft