ArticleComputer methods in applied mechanics and engineering2019
Three-Dimensional Traction Microscopy with a Fiber-Based Constitutive Model.
Article in Computer methods in applied mechanics and engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 28 citations in OpenAlex.
- Deep learning reveals how cells pull, buckle, and navigate fibrous environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- An Inverse Modeling Approach to Estimate Three-Dimensional Aortic Valve Interstitial Cell Stress Fiber Force Levels.Journal of biomechanical engineering · 2023Article
- Stretchable ultrasonic arrays for the three-dimensional mapping of the modulus of deep tissue.Nature biomedical engineering · 2023Article
- Hyperelastic continuum models for isotropic athermal fibrous networks.Interface focus · 2022Article
- Deep-learning-based 3D cellular force reconstruction directly from volumetric images.Biophysical journal · 2022Article
- Multiscale mechanobiology: Coupling models of adhesion kinetics and nonlinear tissue mechanics.Biophysical journal · 2022Article
- Multiscale modeling meets machine learning: What can we learn?Archives of computational methods in engineering : state of the art reviews · 2021Article
- A new 3D finite element-based approach for computing cell surface tractions assuming nonlinear conditions.PloS one · 2021Article
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3 authors at 2 institutions in 1 country.
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Abstract
Tractions exerted by cells on their surroundings play an important role in many biological processes including stem cell differentiation, tumorigenesis, cell migration, cancer metastasis, and angiogenesis. The ability to quantify these tractions is important in understanding and manipulating these processes. Three-dimensional traction force microscopy (3DTFM) provides reliable means of evaluating cellular tractions by first measuring the displacement of fluorescent beads in response to these tractions in the surrounding matrix, and then using this measurement to compute the tractions. However, most applications of 3DTFM assume that the surrounding extra-cellular matrix (ECM) is non-fibrous, despite the fact that in many natural and synthetic environments the ECM contains a significant proportion of fibrous components. Motivated by this, we develop a computational approach for determining tractions, while accounting for the fibrous nature of the ECM. In particular, we make use of a fiber-based constitutive model in which the stress contains contributions from a distribution of nonlinear elastic fibers and a hyperelastic matrix. We solve an inverse problem with the nodal values of the traction vector as unknowns, and minimize the difference between a predicted displacement field, obtained by solving the equations of equilibrium in conjunction with the fiber-based constitutive model, and the measured displacement field at the bead locations. We employ a gradient-based minimization method to solve this problem and determine the gradient efficiently by solving for the appropriate adjoint field. We apply this algorithm to problems with experimentally observed cell geometries and synthetic, albeit realistic, traction fields to gauge its sensitivity to noise, and quantify the impact of using an incorrect constitutive model: the so-called model error. We conclude that the approach is robust to noise, yielding about 10% error in tractions for 5% displacement noise. We also conclude that the impact of model error is significant, where using a nonlinear exponential hyperelastic model instead of the fiber-based model, can lead to more than 100% error in the traction field. These results underline the importance of using appropriate constitutive models in 3DTFM, especially in fibrous ECM constructs.
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