A Data-Driven Computational Model for Engineered Cardiac Microtissues

Engineered heart tissues (EHTs) present a potential solution to some of the current challenges in the treatment of heart disease; however, the development of mature, adult-like cardiac tissues remains elusive. Mechanical stimuli have been observed to improve whole-tissue function and cardiomyocyte (CM) maturation, although our ability to fully utilize these mechanisms is hampered, in part, by our incomplete understanding of the mechanobiology of EHTs. In this work, we leverage the experimental data produced by a mechanically tunable experimental setup to generate tissue-specific computational models of EHTs. Using imaging and functional data, our modeling pipeline generates models with tissue-specific ECM and myofibril structure, allowing us to estimate CM active stress. We use this experimental and modeling pipeline to study different mechanical environments, where we contrast the force output of the tissue with the computed active stress of CMs. We show that the significant differences in measured experimental forces can largely be explained by the levels of myofibril formation achieved by the CMs in the distinct mechanical environments, with active stress showing more muted variations across conditions. The presented model also enables us to dissect the relative contributions of myofibrils and extracellular matrix to tissue force output, a task difficult to address experimentally. These results highlight the importance of tissue-specific modeling to augment EHT experiments, providing deeper insights into the mechanobiology driving EHT function.Comment: 19 pages, 7 figure

Mechanically programming anisotropy in engineered muscle with actuating extracellular matrices

Published versio

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, requires little to no parameter tuning, and runs quickly on a personal computer. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.Comment: 16 main pages, 7 main figures, Supplementary Information included as appendice

Mechanical MNIST - Cahn-Hilliard

The associated paper “Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset” is forthcoming. All code necessary to reproduce this dataset is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Cahn-Hilliard). For questions, please contact Emma Lejeune ([email protected]).The Mechanical MNIST Cahn-Hilliard dataset contains the results of 104,813 Finite Element simulations of a heterogeneous material domain subject to large equibiaxial extension deformation. The heterogeneous domain patterns are generated from a Finite Element implementation of the Cahn-Hilliard equation. Different stripe and circle patterns are obtained by varying four simulation parameters: the initial concentration, the grid size on which the concentration is initialized, parameter $\lambda$, and $b$, the peak-to-valley value of the symmetric double-well chemical free-energy function. Binary bitmap images of 400 x 400 pixels are converted into two-dimensional meshed domains of binary material using the OpenCV library, Pygmsh, and Gmsh 4.6.0. We also include in this dataset the 104,813 patterns (37,523 from case 1, 37,680 from case 2, and 29,610 from case 3) used in the Finite Element simulations stored as binary images in text files. After pattern generation, the material domain is modeled as a unit square of Neo-Hookean binary material (high concentration areas correspond to Young's Modulus 10, low concentration areas correspond to Young's Modulus 1). For equibiaxial extension, each of the four edges of the domain is displaced to 50% of the initial domain size in the direction of the outward normal to the surface with fixed displacements (d = [0.0,0.001,0.1,0.2,0.3,0.4,0.5]). Here we provide the simulation results consisting of the following: (1) change in strain energy reported at each level of applied displacement, (2) total reaction force at the four boundaries reported at each level of applied displacement, and (3) full field displacement reported at the final applied displacement d=0.5. All Finite Element simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations (both pattern generation simulations and equibiaxial extension simulations) is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Cahn-Hilliard). The enclosed document “description.pdf'” contains additional details

Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize, and difficult to simulate. Recently, machine learning-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train machine learning (ML) models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on a dataset of simulations that capture the range of spatial heterogeneity present in the material of interest. However, when it comes to applying these techniques to biological tissue more broadly, there is a major limitation: the relevant microstructural patterns are both challenging to obtain and difficult to analyze. Consequently, the number of useful examples available to characterize the input domain under study is limited. In this work, we investigate the efficacy of ML-based generative models as well as procedural methods as a tool for augmenting limited input pattern datasets. We find that a Style-based Generative Adversarial Network with adaptive discriminator augmentation is able to successfully leverage just 1,000 example patterns to create the most authentic generated patterns. In general, diverse generated patterns with adequate resemblance to the real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access dataset of Finite Element Analysis simulations based on Cahn-Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here.Comment: 13 pages, 6 figure

Optimization of the design of a barbed suture for flexor tendon repair using extended finite element analysis

Aim: Use of barbed sutures for flexor tendon repair is a promising technique. These sutures lie within the substance of the tendon, avoiding the need of external knots and so improving tendon gliding. The load is dispersed equally along the length of the barbed suture, decreasing the possibility of rupture. The purpose of this article is to propose enhanced suture geometry by comparing different cross-sectional configurations, barb cut angles and cut depths using the finite element method.Methods: Inspired by the geometry of flexor tendons, an elliptical cross-sectional wire was investigated. Mechanical behavior of five different aspect ratios (ρ = 1/3, 1/2, 1, 2, 3), three different cut angles (150°, 154°, 160°) and three cut depths (0.07-mm, 0.12-mm, 0.18-mm) were studied via extended finite element analysis using ABAQUS, for two different loading conditions: one to assess the strength of the suture and the second to evaluate the strength of a single barb. An extended finite element method has been implemented on ABAQUS to predict crack growth in viscoelastic material.Results: Based on these results, an elliptical suture having an aspect ratio of 1/2, 160° of cut angle, and 0.12-mm of cut depth is recommended.Conclusion: Barbed sutures are a good option for tendon repair. Our experiments assessed the mechanical performance of barbed sutures and suggested an optimized suture geometry for a single-stranded repair technique