Robustness Analysis of a Synthetic Translational Resource Allocation Controller

Recent research in Synthetic Biology has highlighted the potential of translational resource allocation controllers to improve circuit modularity by dynamically allocating finite cellular resources in response to fluctuating circuit demands. The design of such controllers is complicated by the significant levels of parametric uncertainty that arise in their biological implementations. Tools from robust control, such as µ-analysis, can be used to determine the robustness of controller designs to parametric uncertainty, but require further development to allow their application to biomolecular control systems, which are typically highly non-linear, and contain multiple uncertainties that cannot be represented using the standard linear fractional transformation formalism. Here, we show how an LFT (Linear Fractional Transformation)-free formulation of the µ-analysis problem can be used to analyse and compare the robustness of alternative potential implementations of a translational resource allocation controller that utilises orthogonal ‘circuit-specific’ ribosomes to translate circuit genes. Our results provide useful guidelines for the construction of robust resource allocation circuitry for multiple future biotechnological applications

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

Developing a graduate training program in Synthetic Biology: SynBioCDT

This article presents the experience of a team of students and academics in developing a post-graduate training program in the new field of Synthetic Biology. Our Centre for Doctoral Training in Synthetic Biology (SynBioCDT) is an initiative funded by the United Kingdom's Research Councils of Engineering and Physical Sciences (EPSRC), and Biotechnology and Biological Sciences (BBSRC). SynBioCDT is a collaboration between the Universities of Oxford, Bristol and Warwick, and has been successfully running since 2014, training 78 students in this field. In this work, we discuss the organization of the taught, research and career development training. We also address the challenges faced when offering an interdisciplinary program. The article concludes with future directions to continue the development of the SynBioCDT

Developing a graduate training program in Synthetic Biology: SynBioCDT

This article presents the experience of a team of students and academics in developing a post-graduate training program in the new field of Synthetic Biology. Our Centre for Doctoral Training in Synthetic Biology (SynBioCDT) is an initiative funded by the United Kingdom's Research Councils of Engineering and Physical Sciences (EPSRC), and Biotechnology and Biological Sciences (BBSRC). SynBioCDT is a collaboration between the Universities of Oxford, Bristol and Warwick, and has been successfully running since 2014, training 78 students in this field. In this work, we discuss the organization of the taught, research and career development training. We also address the challenges faced when offering an interdisciplinary program. The article concludes with future directions to continue the development of the SynBioCDT