Dept. Applied Physics, Stanford University, USA
Dept. Genetics, Harvard Medical School, USA
25 July 2013, Thursday, 14:40
Cavid Erginsoy Seminar Room, Physics Department, 3rd floor
Abstract: Physics has a long tradition of beginning with a purely empirical and descriptive view of the world and using these observations to build a theoretical framework that allows prediction of the properties of a wide variety of physical phenomena. In biosciences, however, due to technological limitations and the very complex nature of life, we have been gathering information at the empirical regime of knowledge with few theoretical frameworks that provide predictive power. However, many technical advances are rapidly lowering the cost of data acquisition and analysis. Therefore, utilizing the technological advances and quantitative approaches to develop a predictive framework and identify the fundamental principles underlying biological phenomena will direct the future of life sciences. Use of new quantitative approaches to understand the principles behind cellular phenomena is a growing area of life science. A prominent example of a widespread cellular phenomenon is the irreversible transition corresponding the point of commitment to the mitotic cell cycle. Cells commit to division within the G1 phase of the cell cycle; after cell division, but before initiation of DNA replication. The commitment point was apparently embedded within the expression of the a large portion of all genes. Although many of the components of the genetic network regulating the G1/S transition in mammals do not have well-defined orthologs in yeast, both networks contain multiple positive feedback elements indicating similar network topology. To identify the common principles of the cell cycle commitment, I used yeast and mammalian cells as model organisms and showed that commitment to cell division corresponds to activating a genetically wired positive feedback loop. Thus, my work suggests the "feedback-first motif" as a common working principle for cell cycle decision making in eukaryotes.