Numerical modeling resources were facilitated by Drs. Sung Yi and Hormoz Zareh. This analytical work supports the ongoing research effort funded by the NSF (CBET-0521637), the NIH (EB007077 and MD003350), and the Collins Medical Trust
Journal of Biomechanics
Extracellular matrix -- Physiology, Tissue -- Mechanical properties -- Numerical simulation
Extracellular matrix (ECM) provides a dynamic three-dimensional structure which translates mechanical stimuli to cells. This local mechanical stimulation may direct biological function including tissue development. Theories describing the role of mechanical regulators hypothesize the cellular response to variations in the external mechanical forces on the ECM. The exact ECM mechanical stimulation required to generate a specific pattern of localized cellular displacement is still unknown. The cell to tissue inverse problem offers an alternative approach to clarify this relationship. Developed for structural dynamics, the inverse dynamics problem translates measurements of local state variables (at the cell level) into an unknown or desired forcing function (at the tissue or ECM level). This paper describes the use of eigenvalues (resonant frequencies), eigenvectors (mode shapes), and dynamic programming to reduce the mathematical order of a simplified cell–tissue system and estimate the ECM mechanical stimulation required for a specified cellular mechanical environment. Finite element and inverse numerical analyses were performed on a simple two-dimensional model to ascertain the effects of weighting parameters and a reduction of analytical modes leading toward a solution. Simulation results indicate that the reduced number of mechanical modes (from 30 to 14 to 7) can adequately reproduce an unknown force time history on an ECM boundary. A representative comparison between cell to tissue (inverse) and tissue to cell (boundary value) modeling illustrates the multiscale applicability of the inverse model.
Kim, Wangdo, Derek C. Tretheway, and Sean S. Kohles. "An inverse method for predicting tissue-level mechanics from cellular mechanical input." Journal of biomechanics 42.3 (2009): 395-399.