Fluctuation-response in living cells

Based on our model, we predicted that we could infer cellular response to a stimulus from the behavioral variability in nonstimulated cells. To experimentally establish the existence of a fluctuation-response relationship in the chemotaxis system of E. coli, we used a linear approximation as a general framework for monitoring pre- and post-stimulus switching behavior of individual bacterial motors. This study highlights that under certain conditions, the fundamental fluctuation-response relationship is extensible to living cells not at thermodynamic equilibrium.

This project was the work of former graduate student Heungwon Park.

Using a capillary assay, we studied how the chemotactic response depends on the functioning state determined by the relative chemotactic protein expression level. We found that the chemotactic response was not robust. Instead, it was fine-tuned to various functioning states. Cellular response to the small external stimulus was highest around the average functioning state of wild-type cells.

This project has been published in Current Microbiology.

The Fluctuation-Dissipation Theorem describes the relationship between noise and response in physical systems at thermodynamic equilibrium. Recently, Prost et al. (2009) described a Generalized Fluctuation Dissipation Theorem that applies to systems not at thermodynamic equilibrium, such as gene expression in living cells. We wish to experimentally demonstrate that the cellular response and the spontaneous fluctuations in gene expression are intrinsically coupled. Consequently, we will verify that gene expression response is related to the autocorrelation function of the spontaneous fluctuations of gene expression using: , where K is a constant that may dependent on the genetic background, growth conditions, and function states of the cell.

This project is the work of postdoc Jeff Moffitt.

In our work, we have employed the chemotaxis network responsible for the motion of E. coli bacteria as a model system for the general study of signal transduction networks. We asked whether there were specific molecular events that could cause behavioral variability in an individual cell. By developing a single-cell approach we were able to characterize the design principles of the signal transduction network in bacteria (chemotaxis). We found that the inherent randomness (noise) in the chemical reactions taking place in the intracellular signal processing was a "tuneable" source of behavioral variability. Moreover, the signal transduction network determining cellular behavior was found to be tuned to maximize its sensitivity. We used a combination of computer simulations and genetic experiments to identify the key molecular components that allow the cell to adjust signal transduction sensitivity. We found as a consequence of the network design that maximum sensitivity would be also accompanied with maximum behavioral variability, Korobkova et al., Nature (2004). We believe that such interplay between sensitivity and variability in signal transduction is shared by many other signaling systems.