@phdthesis{Hintsche2018,
  author    = {Hintsche, Marius},
  title     = {Locomotion of a bacterium with a polar bundle of flagella},
  doi       = {10.25932/publishup-42697},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426972},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 108},
  year      = {2018},
  abstract  = {Movement and navigation are essential for many organisms during some parts of their lives. This is also true for bacteria, which can move along surfaces and swim though liquid environments. They are able to sense their environment, and move towards environmental cues in a directed fashion. These abilities enable microbial lifecyles in biofilms, improved food uptake, host infection, and many more. In this thesis we study aspects of the swimming movement - or motility - of the soil bacterium (P. putida). Like most bacteria, P. putida swims by rotating its helical flagella, but their arrangement differs from the main model organism in bacterial motility research: (E. coli). P. putida is known for its intriguing motility strategy, where fast and slow episodes can occur after each other. Up until now, it was not known how these two speeds can be produced, and what advantages they might confer to this bacterium. Normally the flagella, the main component of thrust generation in bacteria, are not observable by ordinary light microscopy. In order to elucidate this behavior, we therefore used a fluorescent staining technique on a mutant strain of this species to specifically label the flagella, while leaving the cell body only faintly stained. This allowed us to image the flagella of the swimming bacteria with high spacial and temporal resolution with a customized high speed fluorescence microscopy setup. Our observations show that P. putida can swim in three different modes. First, It can swim with the flagella pushing the cell body, which is the main mode of swimming motility previously known from other bacteria. Second, it can swim with the flagella pulling the cell body, which was thought not to be possible in situations with multiple flagella. Lastly, it can wrap its flagellar bundle around the cell body, which results in a speed wich is slower by a factor of two. In this mode, the flagella are in a different physical conformation with a larger radius so the cell body can fit inside. These three swimming modes explain the previous observation of two speeds, as well as the non strict alternation of the different speeds. Because most bacterial swimming in nature does not occur in smoothly walled glass enclosures under a microscope, we used an artificial, microfluidic, structured system of obstacles to study the motion of our model organism in a structured environment. Bacteria were observed in microchannels with cylindrical obstacles of different sizes and with different distances with video microscopy and cell tracking. We analyzed turning angles, run times, and run length, which we compared to a minimal model for movement in structured geometries. Our findings show that hydrodynamic interactions with the walls lead to a guiding of the bacteria along obstacles. When comparing the observed behavior with the statics of a particle that is deflected with every obstacle contact, we find that cells run for longer distances than that model. Navigation in chemical gradients is one of the main applications of motility in bacteria. We studied the swimming response of P. putida cells to chemical stimuli (chemotaxis) of the common food preservative sodium benzoate. Using a microfluidic gradient generation device, we created gradients of varying strength, and observed the motion of cells with a video microscope and subsequent cell tracking. Analysis of different motility parameters like run lengths and times, shows that P. putida employs the classical chemotaxis strategy of E. coli: runs up the gradient are biased to be longer than those down the gradient. Using the two different run speeds we observed due to the different swimming modes, we classify runs into `fast' and `slow' modes with a Gaussian mixture model (GMM). We find no evidence that P. putida's uses its swimming modes to perform chemotaxis. In most studies of bacterial motility, cell tracking is used to gather trajectories of individual swimming cells. These trajectories then have to be decomposed into run sections and tumble sections. Several algorithms have been developed to this end, but most require manual tuning of a number of parameters, or extensive measurements with chemotaxis mutant strains. Together with our collaborators, we developed a novel motility analysis scheme, based on generalized Kramers-Moyal-coefficients. From the underlying stochastic model, many parameters like run length etc., can be inferred by an optimization procedure without the need for explicit run and tumble classification. The method can, however, be extended to a fully fledged tumble classifier. Using this method, we analyze E. coli chemotaxis measurements in an aspartate analog, and find evidence for a chemotactic bias in the tumble angles.},
  language  = {en}
}
@phdthesis{Schindler2023,
  author    = {Schindler, Daniel},
  title     = {Mathematical modeling and simulation of protrusion-driven cell dynamics},
  doi       = {10.25932/publishup-61327},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613275},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 161},
  year      = {2023},
  abstract  = {Amoeboid cell motility takes place in a variety of biomedical processes such as cancer metastasis, embryonic morphogenesis, and wound healing. In contrast to other forms of cell motility, it is mainly driven by substantial cell shape changes. Based on the interplay of explorative membrane protrusions at the front and a slower-acting membrane retraction at the rear, the cell moves in a crawling kind of way. Underlying these protrusions and retractions are multiple physiological processes resulting in changes of the cytoskeleton, a meshwork of different multi-functional proteins. The complexity and versatility of amoeboid cell motility raise the need for novel computational models based on a profound theoretical framework to analyze and simulate the dynamics of the cell shape. The objective of this thesis is the development of (i) a mathematical framework to describe contour dynamics in time and space, (ii) a computational model to infer expansion and retraction characteristics of individual cell tracks and to produce realistic contour dynamics, (iii) and a complementing Open Science approach to make the above methods fully accessible and easy to use. In this work, we mainly used single-cell recordings of the model organism Dictyostelium discoideum. Based on stacks of segmented microscopy images, we apply a Bayesian approach to obtain smooth representations of the cell membrane, so-called cell contours. We introduce a one-parameter family of regularized contour flows to track reference points on the contour (virtual markers) in time and space. This way, we define a coordinate system to visualize local geometric and dynamic quantities of individual contour dynamics in so-called kymograph plots. In particular, we introduce the local marker dispersion as a measure to identify membrane protrusions and retractions in a fully automated way. This mathematical framework is the basis of a novel contour dynamics model, which consists of three biophysiologically motivated components: one stochastic term, accounting for membrane protrusions, and two deterministic terms to control the shape and area of the contour, which account for membrane retractions. Our model provides a fully automated approach to infer protrusion and retraction characteristics from experimental cell tracks while being also capable of simulating realistic and qualitatively different contour dynamics. Furthermore, the model is used to classify two different locomotion types: the amoeboid and a so-called fan-shaped type. With the complementing Open Science approach, we ensure a high standard regarding the usability of our methods and the reproducibility of our research. In this context, we introduce our software publication named AmoePy, an open-source Python package to segment, analyze, and simulate amoeboid cell motility. Furthermore, we describe measures to improve its usability and extensibility, e.g., by detailed run instructions and an automatically generated source code documentation, and to ensure its functionality and stability, e.g., by automatic software tests, data validation, and a hierarchical package structure. The mathematical approaches of this work provide substantial improvements regarding the modeling and analysis of amoeboid cell motility. We deem the above methods, due to their generalized nature, to be of greater value for other scientific applications, e.g., varying organisms and experimental setups or the transition from unicellular to multicellular movement. Furthermore, we enable other researchers from different fields, i.e., mathematics, biophysics, and medicine, to apply our mathematical methods. By following Open Science standards, this work is of greater value for the cell migration community and a potential role model for other Open Science contributions.},
  language  = {en}
}