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. Here, we report the construction of synthetic transcriptional cascades comprising one, two, and three repression stages. These model systems enable us to analyze sensitivity and noise propagation as a function of network complexity. We 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. Although 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 demonstrating the above network properties correlate well with simulations of a simple mathematical model of the system. gene regulation ͉ gene network ͉ low-pass filter ͉ stochastic R egulatory cascades are ubiquitous in biological systems. For example, Escherichia coli and Saccharomyces cerevisiae regulatory networks contain transcriptional cascades with two or more stages (1-3). Many signal transduction programs and protein kinase pathways also take advantage of cascaded processes to regulate activities within living cells (4-6). In general, regulatory cascades exhibit several important features (7-8). Protein cascades provide an ultrasensitive ''all-or-none'' response to graded inputs where very small changes in input stimuli switch the output between low and high levels (9-10). Cascades direct temporal programs of successive gene expression as observed in the formation of flagella in E. coli (11), sporulation in budding yeast (12), or regulatory pathways that control bacterial cell cycle (13). In multicellular organisms, such as Drosophila and sea urchin, developmental programs require elaborate temporal ordering of events, often orchestrated by cascaded processes (14-15).Regulatory cascades are frequently found within more complex networks that incorporate additional control mechanisms [i.e., feed forward loops (3, 16), feedback (17), checkpoints (18), and single-input modules (3)]. A valuable approach to studying the properties of recurring network motifs is to decouple them as much as possible from other genetic regulatory elements (19-21). Examining network behavior in model systems can help discover the valuable properties and limitations of these motifs. Recent experimental studies of two-stage transcriptional cascades have examined quantitatively their steady-state sensitivity, § temporal programming (1), and noise properties (23). In addition, ultrasensitivity and attenuation of noise in longer cascades have been analyze...
Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling-High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.
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