Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present

Stochastic model of transcription initiation of closely spaced promoters in escherichia coli

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaThe regulatory mechanisms of transcription allow organisms to quickly adapt to changes in their environment and often act during transcription initiation. Here, a stochastic model of transcription initiation at the nucleotide level is proposed to study the dynamics of RNA production in closely spaced promoters and their regulatory mechanisms. We study how different arrangements (convergent e divergent), distance between transcription start sites (TSS), and various kinetic parameters affect the dynamics of RNA production. Further, we analyze how the kinetics of various steps in transcription initiation can be regulated by varying locations of repressor binding sites. From the results, we observe that the rate limiting steps have strong influence in the kinetics of RNA production. We find that interferences between RNA polymerases in divergent overlapped and convergent geometries causes the distribution of time intervals between the production of consecutive RNA molecules from each TSS to increase in mean and standard deviation, which leads to stronger fluctuations in the temporal levels of RNA molecules. We observe that small changes in the distance between TSSs can lead to abrupt transitions in the dynamics of RNA production, particularly when this change changes the geometry from overlapped to non-overlapped promoters. From the study of the correlation in the choices of directionality and on the time series of RNA productions we show that by tuning the distances and directions of the two TSS one can obtain both negative and positive correlations. We further show that distinct repression mechanisms of transcription initiation in steps such as the open and closed complex formation and promoter escape have different effects on the dynamics of RNA production. The study of these models will help the study of how genetic circuits have evolved and assist in designing artificial genetic circuits with desired dynamics