Synthetic biology may be viewed as an effort to establish, formalize, and develop an engineering discipline in the context of biological systems. The ability to tune the properties of individual components is central to the process of system design in all fields of engineering, and synthetic biology is no exception. A large and growing number of approaches have been developed for tuning the responses of cellular systems, and here we address specifically the issue of tuning the rate of response of a system: given a system where an input affects the rate of change of an output, how can the shape of the response curve be altered experimentally? This affects a system’s dynamics as well as its steady-state properties, both of which are critical in the design of systems in synthetic biology, particularly those with multiple components. We begin by reviewing a mathematical formulation that captures a broad class of biological response curves and use this to define a standard set of varieties of tuning: vertical shifting, horizontal scaling, and the like. We then survey the experimental literature, classifying the results into our defined categories, and organizing them by regulatory level: transcriptional, post-transcriptional, and post-translational.

The growth rate and carrying capacity of a cell population are key to the characterization of the population's viability and to the quantification of its responses to perturbations such as drug treatments. Accurate estimation of these parameters necessitates careful analysis. Here, we present a rigorous mathematical approach for the robust analysis of cell count data, in which all the experimental stages of the cell counting process are investigated in detail with the machinery of Bayesian probability theory. We advance a flexible theoretical framework that permits accurate estimates of the growth parameters of cell populations and of the logical correlations between them. Moreover, our approach naturally produces an objective metric of avoidable experimental error, which may be tracked over time in a laboratory to detect instrumentation failures or lapses in protocol. We apply our method to the analysis of cell count data in the context of a logistic growth model by means of a user-friendly computer program that automates this analysis, and present some samples of its output. Finally, we note that a traditional least squares fit can provide misleading estimates of parameter values, because it ignores available information with regard to the way in which the data have actually been collected.

It is increasingly practical to co-opt many native cellular components into use as elements of synthetic biological systems. We present the design and experimental investigation of the first exogenous genetic construct to be successfully targeted by RNA activation, a phenomenon whereby small double-stranded RNAs increase gene expression from sequence-similar promoters by a mechanism thought to be related to that of RNA interference. Our selection of activating RNA candidates was informed by a custom-written computer program designed to choose target sites in the promoter of interest according to a set of empirical optimality criteria drawn from prior research. Activating RNA candidates were assessed for activity against two exogenously derived target promoters, with successful candidates being subjected to further rounds of validation as a precaution against potential off-target effects. A genetic platform was assembled that allowed activating RNA candidates to be simultaneously screened both for positive activity on the target reporter gene and for possible nonspecific effects on cell metabolism. Several candidate sequences were tested to appraise the utility of this platform, with the most successful achieving a moderate activation level with minimal off-target effects.