IDBR: Development of CytoIQ, an Adaptive Cytometer to Measure the Noisy of Dynamics of Gne Expression in Individual Live Cells

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

The Virginia Bioinformatics Institute at Virginia Tech is awarded a grant to develop Cyto.IQ, an adaptive imaging system specifically designed to characterize the noisy dynamics of gene expression and other molecular interactions in individual live cells. Cyto.IQ analyzes microscopic images on the fly to produce statistical plots and other quantitative indicators capturing important parameters of the cell physiology. The instrument has the capability to optimize the frequency of image acquisition and the total number of images taken using machine learning algorithms. It finds an optimal tradeoff between the cost of an experiment and the information it generates using a priori knowledge of the expected gene expression dynamics and previously acquired data. The control software is able to determine what cells to observe and when to observe them to ensure a fast convergence of statistical estimators while minimizing adverse effects of light exposure, and the overall duration of the experiment. Cyto.IQ is specifically designed to meet the needs of systems biologists, bioengineers, or biophysicists who are developing quantitative models of gene networks. Due to the noise affecting gene expression mechanisms, this rapidly growing community of users needs an instrument to observe the state of many individual cells over time. Current methods used to extract this type of data out of time-series of images collected using standard imaging platforms are inherently inefficient. They represent a major obstacle to the refinement of our understanding of the dynamics of cellular processes. Cyto.IQ increases the productivity of scientists working in this field by reducing the time it takes to perform an experiment and the number of experiments needed to collect suitable data sets. The adaptive control software is open source and available from www.cytoiq.org.