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.

Spatial and temporal variation in settlement, growth and condition of the rockfish species Sebastes caurinus and Sebastes carnatus in the Channel Islands region, Santa Barbara, CA

Survival during the early life stages of marine species, including nearshore temperate reef fishes, is typically very low, and small changes in mortality rates, due to physiological and environmental conditions, can have marked effects on survival of a cohort and, on a larger scale, on the success of a recruitment season. Moreover, trade offs between larval growth and accumulation of energetic resources prior to settlement are likely to influence growth and survival until this critical period and afterwards. Rockfish recruitment rates are notoriously variable between years and across geographic locations. Monitoring of rates of onshore delivery of pelagic juveniles (defined here as settlement) of two species of nearshore rockfishes, Sebastes caurinus and Sebastes carnatus, was done between 2003-2009 years using artificial collectors placed at San Miguel and Santa Cruz Island, off Southern California coast. I investigated spatiotemporal variation in settlement rate, lipid content, pelagic larval duration and larval growth of the newly settled fishes; I assessed relationships between birth date, larval growth, early life-history characteristics and lipid content at settlement, considering also interspecific differences; finally, I attempt to relate interannual patterns of settlement and of early life history traits to easily accessible, local and regional indices of ocean conditions including in situ ocean temperature and regional upwelling, sea surface temperature (SST) and Chlorophyll-a (Chl-a) concentration. Spatial variations appeared to be of low relevance, while significant interannual differences were detected in settlement rate, pelagic larval duration and larval growth. The amount of lipid content of the newly settled fishes was highly variable in space and time, but did not differ between the two species and did not show any relationships with early life history traits, indicating that no trade off involved these physiological processes or they were masked by high individual variability in different periods of larval life. Significant interspecific differences were found in the timing of parturition and settlement and in larval growth rates, with S. carnatus growing faster and breeding and settling later than S. caurinus. The two species exhibited also different patterns of correlations between larval growth rates and larval duration. S. carnatus larval duration was longer when the growth in the first two weeks post-hatch was faster, while S. caurinus had a shorter larval duration when grew fast in the middle and in the end of larval life, suggesting different larval strategies. Fishes with longer larval durations were longer in size at settlement and exhibited longer planktonic phase in periods of favourable environmental conditions. Ocean conditions had a low explanatory power for interannual variation in early life history traits, but a very high explanatory power for settlement fluctuations, with regional upwelling strength being the principal indicator. Nonetheless, interannual variability in larval duration and growth were related to great phenological changes in upwelling happened during the period of this study and that caused negative consequences at all trophic levels along the California coast. Despite the low explanatory power of the environmental variables used in this study on the variation of larval biological traits, environmental processes were differently related with early life history characteristics analyzed to species, indicating possible species-specific susceptibility to ocean conditions and local environmental adaptation, which should be further investigated. These results have implications for understanding the processes influencing larval and juvenile survival, and consequently recruitment variability, which may be dependent on biological characteristics and environmental conditions.