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.

Frequency-dependent response of snow in uniaxial compression and comparison with numerical simulation

La determinazione del modulo di Young è fondamentale nello studio della propagazione di fratture prima del rilascio di una valanga e per lo sviluppo di affidabili modelli di stabilità della neve. Il confronto tra simulazioni numeriche del modulo di Young e i valori sperimentali mostra che questi ultimi sono tre ordini di grandezza inferiori a quelli simulati (Reuter et al. 2013)⁠. Lo scopo di questo lavoro è stimare il modulo di elasticità studiando la dipendenza dalla frequenza della risposta di diversi tipi di neve a bassa densità, 140-280 kg m-3. Ciò è stato fatto applicando una compressione dinamica uniassiale a -15°C nel range 1-250 Hz utilizzando il Young's modulus device (YMD), prototipo di cycling loading device progettato all'Istituto per lo studio della neve e delle valanghe (SLF). Una risposta viscoelastica della neve è stata identificata a tutte le frequenze considerate, la teoria della viscoelasticità è stata applicata assumendo valida l'ipotesi di risposta lineare della neve. Il valore dello storage modulus, E', a 100 Hz è stato identificato come ragionevolmente rappresentativo del modulo di Young di ciascun campione neve. Il comportamento viscoso è stato valutato considerando la loss tangent e la viscosità ricavata dai modelli di Voigt e Maxwell. Il passaggio da un comportamento più viscoso ad uno più elastico è stato trovato a 40 Hz (~1.1•10-2 s-1). Il maggior contributo alla dissipazione è nel range 1-10 Hz. Infine, le simulazioni numeriche del modulo di Young sono state ottenute nello stesso modo di Reuter et al.. La differenza tra le simulazioni ed i valori sperimentali di E' sono, al massimo, di un fattore 5; invece, in Reuter et al.⁠, era di 3 ordini di grandezza. Pertanto, i nostri valori sperimentali e numerici corrispondono meglio, indicando che il metodo qui utilizzato ha portato ad un miglioramento significativo.