Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2004.Includes bibliographical references (leaves 103-112).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Proteins are the functional machinery in living cells. Proteins interact with each other and bind to DNA to form so-called gene regulatory networks and in this way regulate the level, location and timing of expression of other proteins. Cells implement feedback loops to create a memory of their gene expression states. In this way, every differentiated cell in a multicellular organism remembers its expression profile throughout its life. On the other hand, biochemical reactions that take place during gene expression involve small numbers of molecules, and are therefore dominated by large concentration fluctuations. This intrinsic noise has the potential to corrupt memory storage and might result in random transitions between different gene expression states. In the first part of my thesis, I will discuss how the fluctuations in gene expression levels are regulated. The results provided the first experimental evidence that cells can regulate noise in their gene expression by tuning their genetic parameters. In the second half of my thesis, I will discuss how cells create memory by experimentally studying a gene regulatory network that implements a positive feedback loop. A positive feedback loop with nonlinear interactions creates two distinct stable gene expression states. A phase diagram, coupled with a mathematical model of the network, was used to quantitatively investigate the biochemical processes in this network. The response of the network depends on its previous history (hysteresis). Despite the fluctuations in the gene expression, the memory of the gene expression state is preserved for a long time for a broad range of system parameters.(cont.) On the other hand, for some of the parameters, noise causes random transitions of the cells between different gene expression states and results in a bimodal response. Finally, the hysteretic response of the natural system is experimentally converted to an ultrasensitive graded response as predicted by our model.by Ertugrul M. Ozbudak.Ph.D
