A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models.

The last decade has seen the emergence of progressively more complex mechanobiological models, often coupling biochemical and biomechanical components. The complexity of these models makes interpretation difficult, and although computational tools can solve model equations, there is considerable potential value in a simple method to explore the interplay between different model components. Pump and system performance curves, long utilized in centrifugal pump selection and design, inspire the development of a graphical technique to depict visually the performance of biochemically-coupled mechanical models. Our approach is based on a biochemical performance curve (analogous to the classical pump curve) and a biomechanical performance curve (analogous to the system curve). Upon construction of the two curves, their intersection, or lack thereof, describes the coupled model's equilibrium state(s). One can also observe graphically how an applied perturbation shifts one or both curves, and thus how the other component will respond, without rerunning the full model. While the upfront cost of generating the performance curve graphic varies with the efficiency of the model components, the easily interpretable visual depiction of what would otherwise be nonintuitive model behavior is valuable. Herein, we outline how performance curves can be constructed and interpreted for biochemically-coupled biomechanical models and apply the technique to two independent models in the cardiovascular space. The performance curve approach can illustrate and help identify weaknesses in model construction, inform user-applied perturbations and fitting procedures to generate intended behaviors, and improve the efficiency of the model generation and application process.

A computational bridge between traction force microscopy and tissue contraction.

Arterial wall active mechanics are driven by resident smooth muscle cells, which respond to biological, chemical, and mechanical stimuli and activate their cytoskeletal machinery to generate contractile stresses. The cellular mechanoresponse is sensitive to environmental perturbations, often leading to maladaptation and disease progression. When investigated at the single cell scale, however, these perturbations do not consistently result in phenotypes observed at the tissue scale. Here, a multiscale model is introduced that translates microscale contractility signaling into a macroscale, tissue-level response. The microscale framework incorporates a biochemical signaling network along with characterization of fiber networks that govern the anisotropic mechanics of vascular tissue. By incorporating both biochemical and mechanical components, the model is more flexible and more broadly applicable to physiological and pathological conditions. The model can be applied to both cell and tissue scale systems, allowing for the analysis of in vitro, traction force microscopy and ex vivo, isometric contraction experiments in parallel. When applied to aortic explant rings and isolated smooth muscle cells, the model predicts that active contractility is not a function of stretch at intermediate strain. The model also successfully predicts cell-scale and tissue-scale contractility and matches experimentally observed behaviors, including the hypercontractile phenotype caused by chronic hyperglycemia. The connection of the microscale framework to the macroscale through the multiscale model presents a framework that can translate the wealth of information already collected at the cell scale to tissue scale phenotypes, potentially easing the development of smooth muscle cell-targeting therapeutics.

A structural bio-chemo-mechanical model for vascular smooth muscle cell traction force microscopy.

Altered vascular smooth muscle cell (VSMC) contractility is both a response to and a driver for impaired arterial function, and the leading experimental technique for quantifying VSMC contraction is traction force microscopy (TFM). TFM involves the complex interaction among several chemical, biological, and mechanical mechanisms, making it difficult to translate TFM results into tissue-scale behavior. Here, a computational model capturing each of the major aspects of the cell traction process is presented. The model incorporates four interacting components: a biochemical signaling network, individual actomyosin fiber bundle contraction, a cytoskeletal network of interconnected fibers, and elastic substrate displacement due to cytoskeletal force. The synthesis of these four components leads to a broad, flexible framework for describing TFM and linking biochemical and biomechanical phenomena on the single-cell level. The model recapitulated available data on VSMCs following biochemical, geometric, and mechanical perturbations. The structural bio-chemo-mechanical model offers a tool to interpret TFM data in new, more mechanistic ways, providing a framework for the evaluation of new biological hypotheses, interpolation of new data, and potential translation from single-cell experiments to multi-scale tissue models.