Accelerating the pace of synthetic biology experiments requires new approaches for rapid prototyping of circuits from individual DNA regulatory elements. However, current testing standards require days to weeks due to cloning and in vivo transformation. In this work, we first characterized methods to protect linear DNA strands from exonuclease degradation in an Escherichia coli based transcription-translation cell-free system (TX-TL), as well as mechanisms of degradation. This enabled the use of linear DNA PCR products in TX-TL. We then compared expression levels and binding dynamics of different promoters on linear DNA and plasmid DNA. We also demonstrated assembly technology to rapidly build circuits entirely in vitro from separate parts. Using this strategy, we prototyped a four component genetic switch in under 8 h entirely in vitro. Rapid in vitro assembly has future applications for prototyping multiple component circuits if combined with predictive computational models.

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

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming stateof-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future. arXiv:1708.06850v2 [cs.LG] 17 Nov 2017 In 1931, B. O. Koopman published a paper showing that the evolution of any set of observables on a dynamical system can be expressed through the action of an infinite dimensional linear operator, the Koopman operator [1]. Because the Koopman operator is a canonical representation of any autonomous dynamical system, in principle, its use can bring to bear linear analysis methods on nonlinear systems. The Koopman operator is especially powerful for inferring properties of dynamical systems that are either partially or completely unknown or that are too complex to express using standard methods in analysis. Examples of such systems include biological networks, extremely large physical systems (which are intractable to analyze as white-box models), social networks, cyber-physical communication networks, and distributed computing systems that are subject to varying degrees of uncertainty. For this reason, the Koopman operator has gained attention as an effective tool for data-driven model discovery. The Koopman operator provides a data-driven model for comparing the asymptotic behavior of dynamical systems [3], specifically as a function of its spectra [3, 4]. Various algorithms have extended these methods using dynamic and extended dynamic mode decomposition, both for autonomous and controlled systems [5, 6, 7].The prevailing method for learning the Koopman operator from data is dynamic mode decomposition [4]. Dynamic mode decomposition is the process of identifying a linear operator from temporally or spatially-linked data, ultimately with the objective of characterizing the spectrum of the operator.There are many variants of dynamic mode decomposition, but the most recent advances in Koopman operator learning have emerged from extended dynamic mode decomposition [5].In extended dynamic mode decomposition, the idea is to lift the set of system observables from its native vector space into a higher di...

Highlights d Compositional context affects gene-induction response in synthetic gene networks d Supercoiling models can explain the experimental effects of compositional context d Relaxing positive supercoiling with gyrase reduces compositional context effects d We use compositional context effects to design and build an improved toggle switch