<p>Driven by the development of new technologies and an ever expanding knowledge base of molecular and cellular function, Biology is rapidly gaining the potential to develop into a veritable engineering discipline - the so-called `era of synthetic biology' is upon us. Designing biological systems is advantageous because the engineer can leverage existing capacity for self-replication, elaborate chemistry, and dynamic information processing. On the other hand these functions are complex, highly intertwined, and in most cases, remain incompletely understood. Brazenly designing within these systems, despite large gaps in understanding, engenders understanding because the design process itself highlights gaps and discredits false assumptions. </p><p>Here we cover results from design projects that span several scales of complexity. First we describe the adaptation and experimental validation of protein functional assays on minute amounts of material. This work enables the application of cell-free protein expression tools in a high-throughput protein engineering pipeline, dramatically increasing turnaround time and reducing costs. The parts production pipeline can provide new building blocks for synthetic biology efforts with unprecedented speed. Tools to streamline the transition from the in vitro pipeline to conventional cloning were also developed. Next we detail an effort to expand the scope of a cysteine reactivity assay for generating information-rich datasets on protein stability and unfolding kinetics. We go on to demonstrate how the degree of site-specific local unfolding can also be determined by this method. This knowledge will be critical to understanding how proteins behave in the cellular context, particularly with regards to covalent modification reactions. Finally, we present results from an effort to engineer bacterial cell suicide in a population-dependent manner, and show how an underappreciated facet of plasmid physiology can produce complex oscillatory dynamics. This work is a prime example of engineering towards understanding.</p> / Dissertation

The objective of the field of Synthetic Biology is to implement novel functionalities in a biological context or redesign existing biological systems. To achieve this, it employs tried and tested engineering principles, such as standardisation and the design-build-test cycle. A crucial part of this process is the convergence of modelling and experiment. The aim of this thesis is to improve the design principles employed by Synthetic Biology in the context of Gene Regulatory Networks (GRNs). Small Ribonucleic Acids (sRNAs), in particular, are focussed on as a mechanism for post-transcriptional expression regulation, as they present great potential as a tool to be harnessed in GRNs. Modelling sRNA regulation and its interaction with its associated chaperone Host-Factor of Bacteriophage Q&beta; (Hfq) is investigated. Inclusion of Hfq is found to be necessary in stochastic models, but not in deterministic models. Secondly, feedback is core to the thesis, as it presents a means to scale-up designed systems. A linear design framework for GRNs is then presented, focussing on Transcription Factor (TF) interactions. Such frameworks are powerful as they facilitate the design of feedback. The framework supplies a block diagram methodology for visualisation and analysis of the designed circuit. In this context, phase lead and lag controllers, well-known in the context of Control Engineering, are presented as genetic motifs. A design example, employing the genetic phase lag controller, is then presented, demonstrating how the developed framework can be used to design a genetic circuit. The framework is then extended to include sRNA regulation. Four GRNs, demonstrating the simplest forms of genetic feedback, are then modelled and implemented. The feedback occurs at three different levels: autoregulation, through an sRNA and through another TF. The models of these GRNs are inspired by the implemented biological topologies, focussing on steady state behaviour and various setups. Both deterministic and stochastic models are studied. Dynamic responses of the circuits are also briefly compared. Data is presented, showing good qualitative agreement between models and experiment. Both culture level data and cell population data is presented. The latter of these is particularly useful as the moments of the distributions can be calculated and compared to results from stochastic simulation. The fit of a deterministic model to data is attempted, which results in a suggested extension of the model. The conclusion summarises the thesis, stating that modelling and experiment are in good qualitative agreement. The required next step is to be able to predict behaviour quantitatively.