Computational control of gene expression in individual yeast using reactive microscopy.

Abstract

Biotechnology is rapidly improving, largely due to our ability to manipulate genetic material and efficiently measure biological processes. This talk will describe our advances in computational methods and experimental platforms to probe and control stochastic biological processes within individual cells under a microscope. I will describe a novel optogenetics-based approach in which cells are segmented and controlled in real time. An optogenetically regulated gene in each yeast cell under the microscope is modeled using the Chemical Master Equation and controlled independently using a digital micromirror device to send light to only that cell. I will briefly describe the open source python-based software we developed, called MicroMator, that enabled this work, and show how we used it for optogenetically controlled recombination and adaptive illumination of a photobleaching fluorophore. This work brings together stochastic modeling of gene regulation, machine learning based image processing, and automated microscopy to provide methods for understanding basic cellular behavior.