Engineering Robust and Programmable Biological Systems

The ability to engineer programmable biological systems using complex artificial gene networks has great potential to unlock important innovative solutions to many biotechnological challenges. While cells have been engineered to implement complex information processing algorithms and to produce food, materials, and pharmaceuticals, many innovative applications are yet to be realized due to our poor understanding of how robust, reliable, and predictable artificial gene circuits are built. In this work, we demonstrate that robust complex cellular behaviors (e.g., bistability and gene expression dynamics) can be achieved by engineering gene regulatory architecture and increasing the complexity of genetic networks. We further demonstrate that increasing demand for cellular resources causes resource-associated interference among noninteracting genetic devices of various complexities. Importantly, we show that feedback systems can be engineered to enhance the robustness and reliability of genetic circuits by reducing such resource-associated interference among independent circuits. Taken together, this work contributes to understanding the design principles that govern biological robustness and represents an important step towards construction of robust, tunable, reliable, and predictable complex artificial genetic circuits for a wide range of biotechnological applications

Protein dimerization generates bistability in positive feedback loops

Bistability plays an important role in cellular memory and cell fate determination. A positive feedback loop can generate bistability if it contains ultrasensitive molecular reactions. It is often difficult to detect bistability based on such molecular mechanisms due to its intricate interaction with cellular growth. We constructed transcriptional feedback loops in yeast. To eliminate growth alterations, we reduced the protein levels of the transcription factors by tuning the translation rates over two orders of magnitude with designed RNA stem-loops. We modulated two ultrasensitive reactions, homodimerization and the cooperative binding of the transcription factor to the promoter. Either of them is sufficient to generate bistability on its own and when acting together, a particularly robust bistability emerges. This bistability persists even in the presence of a negative feedback loop. Since protein homodimerization is ubiquitous, it is likely to play a major role in the behavior of regulatory networks

Prediction of Cellular Burden with Host--Circuit Models

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modelling paradigm can facilitate fast and robust design cycles in synthetic biology

Recognizing and engineering digital-like logic gates and switches in gene regulatory networks

A central aim of synthetic biology is to build organisms that can perform useful activities in response to specified conditions. The digital computing paradigm which has proved so successful in electrical engineering is being mapped to synthetic biological systems to allow them to make such decisions. However, stochastic molecular processes have graded input-output functions, thus, bioengineers must select those with desirable characteristics and refine their transfer functions to build logic gates with digital-like switching behaviour. Recent efforts in genome mining and the development of programmable RNA-based switches, especially CRISPRi, have greatly increased the number of parts available to synthetic biologists. Improvements to the digital characteristics of these parts are required to enable robust predictable design of deeply layered logic circuits

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production