Much has been learned over the last century about how living cells function, replicate, respond to stimuli and adapt to changing conditions. Data cataloging the involved molecular components, receptors, channels, messengers, regulatory enzymes, structure, as well as the numerous identified interactions is growing. With the parts list and interactions in hand, maps of interacting elements can be drawn that present a static view of known or postulated biological interaction pathways. However, to understand the cell as a living system, dynamical content must be added. Properly constructed models will capture the dynamics of complex biological systems in a form that can be rigorously tested and expanded. This project will contribute to a fundamental understanding of the complex, dynamic, spatial-temporal subcellular processes involved in the bacterial cell cycle. The long-term goals of this project are to: 1. To elucidate the detailed mechanisms by which a symmetric cell cycle can progress towards an asymmetric outcome, in other words, differentiate. 2. To discover new cell cycle regulatory effects due to the time dependence and 3 dimensional positioning of the bacterial chromosome and transcription products throughout the cell. 3. To generate data needed to determine the control system architecture suitable for use in models that formalize the dynamics of cell cycle progression with computer based simulation. Trained in physics and mathematics, the applicant will now apply his skills to biology. The proposed award will provide advanced training in experimental and computational biology at Stanford University. The goal is to emerge from this transitional period as an independent investigator able to lead a multidisciplinary team in an integrated approach to studying complex biological systems relevant to human health and well-being.
| Christen, Beat; Abeliuk, Eduardo; Collier, John M et al. (2011) The essential genome of a bacterium. Mol Syst Biol 7:528 |
| Christen, Beat; Fero, Michael J; Hillson, Nathan J et al. (2010) High-throughput identification of protein localization dependency networks. Proc Natl Acad Sci U S A 107:4681-6 |
| Fero, Michael; Pogliano, Kit (2010) Automated quantitative live cell fluorescence microscopy. Cold Spring Harb Perspect Biol 2:a000455 |
