Double-stimulus sensitive (temperature and pH) self-assemblable polymers

The rheological properties of block co-polymers in water solution at different pH have been investigated. The block copolymers are based on different architectures containing poly(ethylene glycol), poly(propylene glycol) and different blocks of polymer that change their hydrophobic/hydrophilic behavior as a function of pH. The polymer chains of the starting material were extended at their functional ends with the pH-sensitive units using ATRP; this mechanism of controlled radical polymerization was chosen because of the need to minimize polydispersity and avoid transfer reactions possibly leading to homopolymeric inpurities. The starting material were modified in order to use them as macroinitiator for ATRP. The kinetic of each ATRP reaction has been investigated, in order to be able to synthesize polymers with different degree of polymerization, stopping the reaction when the desired polymers chain length has been reached. We will use polymer chains with different basicity and degree of polymerization to link any possible effect of their presence to the conditions under which they become hydrophobic. It has been shown that the rate of polymerization changes changing the type of macroinitiator and the type of monomer synthesized. The slowest rate of polymerization is the one with the most hindered monomer synthesized using the macroinitiator with the highest molecular weight. The water solubility of the synthesized polymers changes depending on the pH of the solution and on the structure of the polymers. It has been shown using 1H-NMR that some of the synthesized polymers are capable to self-aggregation in water solution. The self-aggregation and the type of aggregation is influenced from the structure of the polymer and from the pH of the solution. Changing the structure of the polymers and the pH it is possible to obtain different type of aggregates in solution. This aggregates differ for the volume occupied from them, and for their hardness. Rheological measurements have been demonstrated that the synthesized polymers are capable to form gel phases. The gelation temperature changes changing the structure of the aggregates in solution and it is possible to correlate the changing in the gelation temperature with the changing in the structure of the polymer.

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.