Ron MiloView all speakers
Ron Milo is Assistant Professor in the Department of Plant Sciences at the Weizmann Institute of Science.
After a Bachelor degree in Physics and a Master’s in Electrical Engineering, I became mesmerized by living matter. My PhD at the Weizmann Institute dealt with building blocks of biological networks and I then tried to understand evolutionary adaptations as a Fellow at Harvard Medical School Department of Systems Biology.
Today, my lab members and I are passionate about trying to understand the cellular highways of energy and carbon transformations known as central carbon metabolism in quantitative terms. We employ a combination of computational and experimental synthetic biology tools. My research efforts combine three main directions: (1) Understanding the structure and logic of central carbon and energy metabolism in quantitative terms; (2) Synthetic metabolic pathways for carbon fixation; (3) Novel tools facilitating accurate, accessible and collaborative quantitative cell biology.
Protein levels are a dominant factor shaping natural and synthetic biological systems. While proper functioning of metabolic pathways relies on precise control of enzyme levels, the experimental ability to balance the levels of many genes in parallel is a major outstanding challenge. Here, we introduce a rapid and modular method to span the expression space of several proteins in parallel. By combinatorially pairing genes with a compact set of ribosome binding sites we modulate protein abundance by several orders of magnitude. We demonstrate our strategy by using a synthetic operon containing fluorescent proteins to span a three-dimensional color space. Using the same approach we modulate a recombinant carotenoid biosynthesis pathway in E. coli to reveal a diversity of phenotypes, each characterized by a distinct carotenoid accumulation profile. In a single combinatorial assembly we achieve a yield of the industrially valuable compound astaxanthin 4-fold higher than previously reported. The methodology presented here provides an efficient tool for exploring a high-dimensional expression space to locate desirable phenotypes.