Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.

Genetic characterisation and global comparison of ancient Mediterranean Great White Sharks population (Carcharodon carcharias, L.1758)

This study poses as its objective the genetic characterization of the ancient population of the Great White shark, Carcharodon carcharias, L.1758, present in the Mediterranean Sea. Using historical evidence, for the most part buccal arches but also whole, stuffed examples from various national museums, research institutes and private collections, a dataset of 18 examples coming from the Mediterranean Sea has been created, in order to increase the informations regarding this species in the Mediterranean. The importance of the Mediterranean provenance derives from the fact that a genetic characterization of this species' population does not exist, and this creates gaps in the knowledge of this species in the Mediterranean. The genetic characterization of the individuals will initially take place by the extraction of the ancient DNA and the analysis of the variations in the sequence markers of the mitochondrial DNA. This approach has allowed the genetic comparison between ancient populations of the Mediterranean and contemporary populations of the same geographical area. In addition, the genetic characterization of the population of white sharks of the Mediterranean, has allowed a genetic comparison with populations from global "hot spots", using published sequences in online databases (NCBI, GenBank). Analyzing the variability of the dataset, both in terms space and time, I assessed the evolutionary relationships of the Mediterranean population of Great Whites with the global populations (Australia/New Zealand, South Africa, Pacific USA, West Atlantic), and the temporal trend of the Mediterranean population variability. This method based on the sequencing of two portions of mitochondrial DNA genes, markers showed us how the population of Great White Sharks in the Mediterranean, is genetically more similar to the populations of the Australia Pacific ocean, American Pacific Ocean, rather than the population of South Africa, and showing also how the population of South Africa is abnormally distant from all other clusters. Interestingly, these results are inconsistent with the results from tagging of this species. In addition, there is evidence of differences between the ancient population of the Mediterranean with the modern one. This differentiation between the ancient and modern population of white shark can be the result of events impacting on this species occurred over the last two centuries.

Genetic variability and differentiation in the Mediterranean endemic starry ray Raja asterias (Delaroche, 1809)

The present study deal with the population structure and connectivity of the Mediterranean endemic starry ray Raja asterias (Delaroche, 1809) in the Western and Eastern Mediterranean basin. A panel of eight microsatellite loci which cross-amplify in Rajidae (El Nagar, 2010) was used to assess population connectivity and structure. Those aims were investigated by analyzing the genetic variation of 9 population sample for a total of 185 individuals collected during past scientific surveys (MEDITS, GRUND), commercial trawling and also directly at fish markets. The purpose of this thesis is to estimate the genetic divergence occurring between the Mediterranean populations and, in particular, to assess the presence of any barrier (geographic, hydrogeological and biological) to gene flow for this species. Different statistical approaches were performed to reach this aim evaluating both the genetic diversity (nucleotide diversity, allelic richness, observed and expected heterozygosity and Hardy-Weinberg equilibrium test) and the population differentiation patterns (pairwise Fst estimated and population structure analysis). The results obtained from the analysis of the microsatellite dataset suggest a geographic and genetic separation between the starry ray populations of the Mediterranean basin into three or four distinct groups: Western and Eastern Mediterranean basins and Sicilian coast always clustering as an independent group and Algeria which could be or not considered another separate group. The data were discussed from both an evolutionary and a conservation point of view and in relation to previous results obtained by the analysis of mitochondrial marker. A comparison with other Mediterranean demersal skate species was performed in order to better contextualise our results. Finally, our results could offer useful information to protect vulnerable species as R. asterias and developing effective conservation plans in the Mediterranean.