Sensitivity and noise propagation in complex synthetic gene networks

The precise nature of information flow through a biological network, which is governed by factors such as response sensitivities and noise propagation, greatly affects the operation of biological systems. Quantitative analysis of these properties is often difficult in naturally occurring systems, but can be greatly facilitated by studying simple synthetic networks. In this thesis, I report the construction of a library of synthetic gene networks and analyze response sensitivity and noise propagation as a function of network complexity. First, I study a series of transcriptional cascades. I demonstrate experimentally steady state switching behavior that becomes sharper with longer cascades. The regulatory mechanisms that confer this ultrasensitive response both attenuate and amplify phenotypical variations depending on the system's input conditions. While noise attenuation allows the cascade to act as a low-pass filter by rejecting short-lived perturbations in input conditions, noise amplification results in loss of synchrony among a cell population. The experimental results correlate well with the simulations of a mathematical model of the system.;To further investigate the effect of network topology on system behavior, I develop stochastic models to analyze how the strength and delay of negative feedback affect noise propagation and synchrony within a cell population. This analysis indicates that incorporating negative autoregulation to multi-stage transcriptional cascades does not attenuate noise when compared to the original unregulated networks. On the other hand, delayed negative feedback can give rise to oscillatory behavior, a desirable trait for certain biological processes.;The effect of autoregulation on response and noise behavior of one stage and two stage cascades are then experimentally tested. I observe that the role of negative autoregulation in controlling noise behavior is a complex matter. While a highly regulated system attenuates noise, an increase in noise levels is seen in intermediate autoregulatory strengths. These findings reinforce the notion that noise propagation within transcriptional networks is dependent on network topology in a complex fashion and should therefore always be studied within the context of the overall network architecture.