Han Suyue, Nguyen Duy T, Modarres-Sadeghi Yahya, Jiménez Juan Miguel
Department of Mechanical & Industrial Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Department of Biomedical Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
Ann Biomed Eng. 2022 Dec;50(12):1872-1881. doi: 10.1007/s10439-022-03006-y. Epub 2022 Jul 11.
A proper orthogonal decomposition (POD) order reduction method was implemented to reduce the full high dimensional dynamical system associated with a wound healing cell migration assay to a low-dimensional approximation that identified the prevailing cell trajectories. The POD analysis generated POD modes that were representative of the prevalent cell trajectories. The shapes of the POD modes depended on the location of the cells with respect to the wound and exposure to disturbed (DF) or undisturbed (UF) fluid flow where the net flow was in the antegrade direction with a retrograde component or fully antegrade, respectively. For DF and UF, the POD modes of the downstream cells identified trajectories that moved upstream against the flow, while upstream POD modes exhibited sideways cell migrations. In the absence of flow, no major shape differences were observed in the POD modes on either side of the wound. The POD modes also served to reconstruct the cell migration of individual cells. With as few as three modes, the predominant cell migration trajectories were reconstructed, while the level of accuracy increased with the inclusion of more POD modes. The POD order reduction method successfully identified the predominant cell migratory trajectories under static and varying pulsatile fluid flow conditions serving as a first step in the development of artificial intelligence models of cell migration in disease and development.
