While complex dynamic biological networks control gene expression in all living organisms, the forward engineering of comparable synthetic networks remains challenging. The current paradigm of characterizing synthetic networks in cells results in lengthy design-build-test cycles, minimal data collection, and poor quantitative characterization. Cell-free systems are appealing alternative environments, but it remains questionable whether biological networks behave similarly in cell-free systems and in cells. We characterized in a cell-free system the ‘repressilator’, a three-node synthetic oscillator. We then engineered novel three, four, and five-gene ring architectures, from characterization of circuit components to rapid analysis of complete networks. When implemented in cells, our novel 3-node networks produced population-wide oscillations and 95% of 5-node oscillator cells oscillated for up to 72 hr. Oscillation periods in cells matched the cell-free system results for all networks tested. An alternate forward engineering paradigm using cell-free systems can thus accurately capture cellular behavior.DOI: http://dx.doi.org/10.7554/eLife.09771.001

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In this paper, a novel perfect tracking control method based on multirate feedforward control is proposed. The advantages of the proposed method are that: 1) the proposed multirate feedforward controller eliminates the notorious unstable zero problem in designing the discrete-time inverse system; 2) the states of the plant match the desired trajectories at every sampling point of reference input; and 3) the proposed controller is completely independent of the feedback characteristics. Thus, highly robust performance is assured by the robust feedback controller. Moreover, by generalizing the relationship between the sampling period of plant output and the control period of plant input, the proposed method can be applied to various systems with hardware restrictions of these periods, which leads to higher performance. Next, it is shown that the structure of the proposed perfect tracking controller is very simple and clear. Illustrative examples of position control using a dc servomotor are presented, and simulations and experiments demonstrate the advantages of this approach.

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Single-cell bacterial sensors have numerous applications in human health monitoring, environmental chemical detection, and materials biosynthesis. Such bacterial devices need not only the capability to differentiate between combinations of inputs, but also the ability to process signal timing and duration. In this work, we present a two-input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. The temporal logic gate design relies on unidirectional DNA recombination with bacteriophage integrases to detect and encode sequences of input events. When implemented in a chromosomally-modified E. coli strain, we can utilize stochastic single cell responses to predict overall heterogeneous population behavior. We show that a stochastic model can be used to predict final population distributions of this E. coli strain, and thus that final differentiated sub populations can be used to deduce the timing and duration of transient chemical events.

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Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.

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