Cell-free transcription–translation provides a simplified prototyping environment to rapidly design and study synthetic networks. Despite the presence of a well characterized toolbox of genetic elements, examples of genetic networks that exhibit complex temporal behavior are scarce. Here, we present a genetic oscillator implemented in an E. coli-based cell-free system under steady-state conditions using microfluidic flow reactors. The oscillator has an activator–repressor motif that utilizes the native transcriptional machinery of E. coli: the RNAP and its associated sigma factors. We optimized a kinetic model with experimental data using an evolutionary algorithm to quantify the key regulatory model parameters. The functional modulation of the RNAP was investigated by coupling two oscillators driven by competing sigma factors, allowing the modification of network properties by means of passive transcriptional regulation.

The reproduction of emergent behaviors in nature using reaction networks is an important objective in synthetic biology and systems chemistry. Herein, the first experimental realization of an enzymatic reaction network capable of an adaptive response is reported. The design is based on the dual activity of trypsin, which activates chymotrypsin while at the same time generating a fluorescent output from a fluorogenic substrate. Once activated, chymotrypsin counteracts the trypsin output by competing for the fluorogenic substrate and producing a non-fluorescent output. It is demonstrated that this network produces a transient fluorescent output under out-of-equilibrium conditions while the input signal persists. Importantly, in agreement with mathematical simulations, we show that optimization of the pulse-like response is an inherent trade-off between maximum amplitude and lowest residual fluorescence.

Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

BackgroundEvolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes).ResultsThe key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction.ConclusionsIn this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0499-9) contains supplementary material, which is available to authorized users.