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A computational approach to understand phenotypic structure and constitutive mechanics relationships of single cells.

ABSTRACT: The goal of this study is to construct a representative 3D finite element model (FEM) of individual cells based on their sub-cellular structures that predicts cell mechanical behavior. The FEM simulations replicate atomic force microscopy (AFM) nanoindentation experiments on live vascular smooth muscle cells. Individual cells are characterized mechanically with AFM and then imaged in 3D using a spinning disc confocal microscope. Using these images, geometries for the FEM are automatically generated via image segmentation and linear programming algorithms. The geometries consist of independent structures representing the nucleus, actin stress fiber network, and cytoplasm. These are imported into commercial software for mesh refinement and analysis. The FEM presented here is capable of predicting AFM results well for 500 nm indentations. The FEM results are relatively insensitive to both the exact number and diameter of fibers used. Despite the localized nature of AFM nanoindentation, the model predicts that stresses are distributed in an anisotropic manner throughout the cell body via the actin stress fibers. This pattern of stress distribution is likely a result of the geometric arrangement of the actin network.

SUBMITTER: Wood ST

PROVIDER: S-EPMC3594057 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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A computational approach to understand phenotypic structure and constitutive mechanics relationships of single cells.

Wood Scott T ST Dean Brian C BC Dean Delphine D

Annals of biomedical engineering 20121122 3

The goal of this study is to construct a representative 3D finite element model (FEM) of individual cells based on their sub-cellular structures that predicts cell mechanical behavior. The FEM simulations replicate atomic force microscopy (AFM) nanoindentation experiments on live vascular smooth muscle cells. Individual cells are characterized mechanically with AFM and then imaged in 3D using a spinning disc confocal microscope. Using these images, geometries for the FEM are automatically genera ...[more]

PMID: 23180027

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Project description:Primary human alveolar type II (AT II) epithelial cells maintained in Matrigel cultures form alveolar-like cysts (ALCs) using a cytogenesis mechanism that is different from that of other studied epithelial cell types: neither proliferation nor death is involved. During ALC formation, AT II cells engage simultaneously in fundamentally different, but not fully characterized activities. Mechanisms enabling these activities and the roles they play during different process stages are virtually unknown. Identifying, characterizing, and understanding the activities and mechanisms are essential to achieving deeper insight into this fundamental feature of morphogenesis. That deeper insight is needed to answer important questions. When and how does an AT cell choose to switch from one activity to another? Why does it choose one action rather than another? We report obtaining plausible answers using a rigorous, multi-attribute modeling and simulation approach that leveraged earlier efforts by using new, agent and object-oriented capabilities. We discovered a set of cell-level operating principles that enabled in silico cells to self-organize and generate systemic cystogenesis phenomena that are quantitatively indistinguishable from those observed in vitro. Success required that the cell components be quasi-autonomous. As simulation time advances, each in silico cell autonomously updates its environment information to reclassify its condition. It then uses the axiomatic operating principles to execute just one action for each possible condition. The quasi-autonomous actions of individual in silico cells were sufficient for developing stable cyst-like structures. The results strengthen in silico to in vitro mappings at three levels: mechanisms, behaviors, and operating principles, thereby achieving a degree of validation and enabling answering the questions posed. We suggest that the in silico operating principles presented may have a biological counterpart and that a semiquantitative mapping exists between in silico causal events and in vitro causal events.

Dataset Information

A computational approach to understand phenotypic structure and constitutive mechanics relationships of single cells.

Publications

A computational approach to understand phenotypic structure and constitutive mechanics relationships of single cells.

Similar Datasets

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