Spatial modelling of transcription dynamics in bacterial gene regulatory networks

Ph. D. Thesis.In Synthetic biology, researchers can alter the DNA sequence of organisms such that the behaviour to specific inputs is predictable. Regulatory systems have been ‘hacked’ into doing computation, help with bio-production, aid in personalised medicine and providing highly specific sensors. A major bottleneck in current synthetic biology is that models fail to predict system behaviour reliably, causing recent progress to be reliant on the trial and error of model-assisted system designs. One of the reasons for the models to fail is the neglect of Spatial effects. While this neglect simplifies models, recent experimental data shows localised effects. This work shows that only the combination of 3D cytosol diffusion and the 1D sliding along the chromosome of transcription factors can explain localised effects; the modelling transcription factors initial sliding route after formation reproduces experimental results. However, one essential assumption for the model described above is the initial location of a functional transcription factor at the encoding gene. While the coupled transcription and translation in prokaryotes are experimentally verified and can lead to the localisation of Transcription Factor proteins, this localisation must be assumed to be transferred to the active dimer form to reproduce the experiment. To substantiate this assumption, this work expands the limited field of protein dimerisation. A new model is introduced to explain the localisation effect with an extra pathway we call Translation Mediated Dimerisation. Here, the partially formed transcription factors still undergoing translation are thought to meet and form a dimer while still constrained to the mRNA on the other end. Even if this occurs in a minority of events, this can drastically affect non-linear behaviour. This model allows utilisation of localised effects for the rational design of system dynamics otherwise unavailable, expanding the possibilities and increasing the efficiency of synthetic biolog

Computational design and characterisation of synthetic genetic switches

Genetic toggle switches consist of two mutually repressing transcription factors. The switch motif forms the basis of epigenetic memory and is found in natural decision making systems, such as cell fate determination in developmental pathways. A synthetic genetic switch can be used for a variety of applications, like recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. In this thesis, the genetic toggle switch was studied computationally and experimentally. Bayesian model selection methods were used to compare competing model designs of the genetic toggle switch. It was found that the addition of positive feedback loops to the genetic toggle switch increases the parametric robustness of the system. A computational tool based on Bayesian statistics was developed, that can identify regions of parameter space capable of producing multistable behaviour while handling parameter and initial conditions uncertainty. A collection of models of genetic switches were examined, ranging from the deterministic simplified toggle switch to stochastic models containing different positive feedback connections. The design principles behind making a bistable switch were uncovered, as well as those necessary to make a tristable or quadristable switch. Flow Cytometry was used to characterise a known toggle switch plasmid. A computational tool was developed which uses Bayesian statistics to infer model parameter values from flow cytometry data. This tool was used to characterise the toggle switch plasmid and fit a stochastic computational model to experimental data. The work presented here suggests ways in which the construction of genetic switches can be enhanced. The algorithms developed were shown to be useful in synthetic system design as well as parameter inference. The tools developed here can enhance our understanding of biological systems and constitute an important addition to the systems approach to synthetic biology engineerin