Biophysical Techniques to Quantify Tissue Mechanical Forces and Chemical Kinetics

Abstract

This work applies novel inverse modeling techniques to both chemical kinetics in lab-on-chip devices and cell mechanics in developing Drosophila embryos. The aim is to describe these systems quantitatively; to compare tissue tension in normal and mutant embryos in fruit flies and asses the binding of potential toxicants to channel surfaces in microfluidic devices. In both vertebrates and invertebrates, large-scale movements of epithelial sheets are necessary for many embryonic morphogenetic events, and in wound healing. Tension anisotropy was quantified in the Drosophila ectoderm during a major morphogenetic event using a novel force analysis. In order for stable tissue movement to occur, it was found the direction of internal tension anisotropy throughout germ band tissue must oppose external stress from the neighboring amnioserosa tissue at all time points its retraction movement and during the dorsal closure stage following. In mutant hindsight embryos where the amnioserosa apoptoses during retraction, the germ band internal tension was found to maintain this direction. PDMS-based microfluidic devices were found to adsorb chemicals through exposed surfaces creating problematic changes in dose response curves and timing of chemical delivery to cells. Molecular agents used in lab-on-chip devices were tested in recent efforts to quantify PDMS binding. Quantitative relationships for chemical partitioning into PDMS were established through fitting spectroscopic data to a microscopic model of binding kinetics and extracting time dependent adsorption coefficients, saturation amount and forward and reverse rate constants. The relationship between chemical partitioning and select molecular properties was investigated, and a combination of the octanol-water partition coefficient (LogP) and H-bond donor number were shown to be a decent predictor of absorption. Experimental rate constants were used to in a computational fluid dynamics model to predict cellular exposure for continuous and bolus injection of several chemicals in a realistic device geometry.