Hypothesis testing via integrated computer modeling and digital fluorescence microscopy

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy.     

A mathematical model of ageing in yeast

Budding yeast, Saccharomyces cerevisiae, is commonly used as a system to study cellular ageing. Yeast mother cells are capable of only a limited number of divisions before they undergo senescence, whereas newly formed daughters usually have their replicative age "reset" to zero. Accumulation of extrachromosomal ribosomal DNA circles (ERCs) appears to be an important contributor to ageing in yeast, and we describe a mathematical model that we developed to examine this process. We show that an age-related accumulation of ERCs readily explains the observed features of yeast ageing but that in order to match the experimental survival curves quantitatively, it is necessary that the probability of ERC formation increases with the age of the cell. This implies that some other mechanism(s), in addition to ERC accumulation, must underlie yeast ageing. We also demonstrate that the model can be used to gain insight into how an extra copy of the Sir2 gene might extend lifespan and we show how the model makes novel, testable predictions about patterns of age-specific mortality in yeast populations.     

Coordinated Development of Yeast Colonies: Quantitative Modeling of Diffusion‐Limited Growth – Part 2

A mathematical model was developed which described the growth of yeast colonies based on the assumptions that (i) these populations were built up of single cells whose proliferation was (ii) exclusively controlled by nutrient availability in the environment. The model was of a hybrid cellular automaton type and described discrete cells residing on a one‐dimensional lattice as well as on continuously distributed nutrients. Experimental results and numerical calculations were compared to elucidate under which cultivation conditions the diffusion‐limited growth (DLG) was the major construction principle in yeast colonies. Simulations were scaled to the growth of Yarrowia lipolytica and Candida boidinii colonies under carbon and nitrogen limitation. They showed that nutrient‐controlled growth of the individual cells resulted in DLG of the population. Quantitative predictions for the spatio‐temporal development of the cell‐density profile inside a growing yeast mycelium were compared to the growth characteristics of the model yeast mycelia. Only for the carbon‐limited growth of C. boidinii colonies on glucose as the limiting nutrient resource did the DLG model reproduce the cell‐density profile estimated at the end of the cultivation. Under all other cultivation conditions, strong discrepancies between calculations and experimental results were evident precluding DLG as the ruling regulatory mechanism. Thus, whether or not the development of a yeast population could be described by a DLG scenario, was strongly dependent on the particular cultivation conditions and the applied yeast species. In those cases for which the DLG hypothesis failed to explain the observed growth patterns, the underlying assumptions, i.e., the complete absence of nutrient translocation between the individual cells inside the yeast mycelia as well as the exclusively nutrient‐controlled proliferation of the cells, have to be reevaluated. The presented study demonstrated how the mathematical analysis of growth processes in yeast populations could assist the experimental identification of potential regulatory mechanisms.