The dynamics of genetic networks

Abstract

Living cells are made up of networks of interacting genes, proteins and biochemicals. Simple interactions between network components can lead to complex collective dynamics. Cells use these emergent dynamical properties to propagate signals and perform computations, to probe their surroundings and generate appropriate responses. These phenomena can only be understood at the network level, not at the level of individual components. We have explored the behavior of a variety of biological networks, using quantitative experimental measurements to validate the predictions of detailed network models. Signal and noise in genetic networks. Biochemical reactions often involve small numbers of molecules, and are therefore dominated by stochastic fluctuations. We theoretically analyzed the origin of fluctuations in linearized genetic networks, and showed that these fluctuations could be suppressed by certain network architectures. We then experimentally measured the expression of a reporter protein in single cells of an isogenic bacterial population, and found that stochastic mechanisms caused expression levels to vary from cell to cell. We demonstrated that this variability arose due to number fluctuations in transcription and translation processes, and showed that it could be regulated using genetic parameters. Computational properties of genetic networks. Regulatory networks implement intricate computations which determine the response of a cell to external stimuli. The phosphotransferase system of Escherichia coli regulates the metabolism of several sugars. We used a biochemical model of the system to investigate the range of possible metabolic responses it could generate.

(cont.) We then experimentally investigated a simpler sugar uptake system, the lactose utilization network of Escherichia coli. Expression at the lac operon is regulated by a positive feedback loop, causing the system to have a hysteretic bistable response. We used a reporter protein to monitor lac expression in single cells, and mapped the occurrence of bistable behavior as a function of external sugar concentrations. The structure of this phase diagram allowed us to extract detailed information about network architecture. This system provides a simple example of cellular memory and serves as a model for cell differentiation, showing how transient external stimuli can be used to generate sustained internal responses.