Model based dynamics analysis in live cell microtubule images

Background: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. Results: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Conclusion: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior

Mathematical modelling and numerical simulation of the morphological development of neurons

BACKGROUND: The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. METHODS: A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. RESULTS: Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. CONCLUSION: A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity

Phenotypic Heterogeneity and the Evolution of Bacterial Life Cycles

Most bacteria live in colonies, where they often express different cell types. The ecological significance of these cell types and their evolutionary origin are often unknown. Here, we study the evolution of cell differentiation in the context of surface colonization. We particularly focus on the evolution of a ‘sticky’ cell type that is required for surface attachment, but is costly to express. The sticky cells not only facilitate their own attachment, but also that of non-sticky cells. Using individual-based simulations, we show that surface colonization rapidly evolves and in most cases leads to phenotypic heterogeneity, in which sticky and non-sticky cells occur side by side on the surface. In the presence of regulation, cell differentiation leads to a remarkable set of bacterial life cycles, in which cells alternate between living in the liquid and living on the surface. The dominant life stage is formed by the surface-attached colony that shows many complex features: colonies reproduce via fission and by producing migratory propagules; cells inside the colony divide labour; and colonies can produce filaments to facilitate expansion. Overall, our model illustrates how the evolution of an adhesive cell type goes hand in hand with the evolution of complex bacterial life cycles

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved

Computational modelling of pattern formation by myxobacteria

Myxobacteria are social bacteria that are remarkable for their complex life cycle. In vegetative state, when nutrients are available, myxobacteria cooperatively swarm on a solid surface and feed. When exposed to starvation conditions, myxobacteria exhibit multicellular morphogenesis: 10^5-10^6 cells aggregate to form a fruiting body. Due to their unique life cycle, myxobacteria often serve as relatively simple model organism to study multicellular development and morphogenesis. Myxobacteria cells glide on a substratum, periodically reversing direction and interact with surrounding cells of a swarm. During developmental process, myxobacteria cells often form various patterns: clusters of cells, domains of aligned cells, circular aggregates and streams of cells traveling into the aggregates. The goal of the thesis was to formulate a computationally efficient mechanical mass-spring model of a myxobacterium cell and study the importance of mechanical interactions between cells for the pattern formation in myxobacteria populations. In Chapter 2, a basic model was formulated and it was investigated how cell flexibility affects cell alignment in the population in two-dimensions. The model was formulated in terms of experimentally measurable mechanical parameters, such as engine force, bending stiffness, and drag coefficient. It was shown, that a population of rigid cells can align well due to mechanical interactions between cells, but that cell flexibility impedes the alignment. Theoretical estimations of cell flexibility suggest that myxobacteria cells could be too flexible for the population to align due to mechanical interactions. Therefore, in Chapter 3 lateral restriction of cell movement due to contact with the substratum was introduced in the model. It was shown that lateral restriction can increase the ability of a population of flexible cells to align. In Chapter 4 it was studied how reversal period of cells affects population movement patterns. The results indicate that short reversal period results in domains of aligned cells, whereas long reversal period produces cell clusters. Furthermore, the model reveals that in densely packed populations, non-reversing cells can sort themselves due to mechanical interactions to produce streams of cells that travel in the same direction. Chapter 5 introduces short-range guidance forces between the trailing pole of one myxobacterium and the leading pole of another and investigates the resulting patterns. It is shown that certain types of short-range guiding interactions can explain the formation of circular aggregates. In Chapter 6, the model is extended to three-dimensions and simulation outcome is compared with the results obtained in the previous chapters. The three-dimensional model shows that guiding interactions as in Chapter 5 can initiate the formation of unstable mounds. Finally, the thesis Outlook discusses a series of directions in which the current model can be extended to further understand the importance of mechanical interactions between gliding cells on the development of myxobacteria.BiotechnologyApplied Science