Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics.

MOTIVATION: Kinetochores are multiprotein complexes which mediate chromosome attachment to microtubules (MTs) of the mitotic spindle. They regulate MT dynamics during chromosome segregation. Our goal is to identify groups of kinetochore proteins with similar effects on MT dynamics, revealing pathways through which kinetochore proteins transform chemical and mechanical input signals into cues of MT regulation. RESULTS: We have developed a hierarchical, agglomerative clustering algorithm that groups Saccharomyces cerevisiae strains based on MT-mediated chromosome dynamics measured by high-resolution live cell microscopy. Clustering is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics. We have found that the regulation of wildtype MT dynamics varies with cell cycle and temperature, but not with the chromosome an MT is attached to. By clustering the dynamics of mutants, we discovered that the three genes IPL1, DAM1 and KIP3 co-regulate MT dynamics. Our study establishes the clustering of chromosome and MT dynamics by ARMA descriptors as a sensitive framework for the systematic identification of kinetochore protein subcomplexes and pathways for the regulation of MT dynamics. AVAILABILITY: The clustering code, written in Matlab, can be downloaded from http://lccb.scripps.edu. ('download' hyperlink at bottom of website). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Yeast kinetochore microtubule dynamics analyzed by high-resolution three-dimensional microscopy.

We have probed single kinetochore microtubule (k-MT) dynamics in budding yeast in the G1 phase of the cell cycle by automated tracking of a green fluorescent protein tag placed proximal to the centromere on chromosome IV and of a green fluorescent protein tag fused to the spindle pole body protein Spc42p. Our method reliably distinguishes between different dynamics in wild-type and mutant strains and under different experimental conditions. Using our methods we established that in budding yeast, unlike in metazoans, chromosomes make dynamic attachments to microtubules in G1. This makes it possible to interpret measurements of centromere tag dynamics as reflecting k-MT dynamics. We have examined the sensitivity of our assay by studying the effect of temperature, exposure to benomyl, and a tubulin mutation on k-MT dynamics. We have found that lowering the temperature and exposing cells to benomyl attenuate k-MT dynamics in a similar manner. We further observe that, in contrast to previous reports, the mutant tub2-150 forms k-MTs that depolymerize faster than wild type. Based on these findings, we propose high-resolution light microscopy of centromere dynamics in G1 yeast cells as a sensitive assay for the regulation of single k-MT dynamics.

The Karyote physico-chemical genomic, proteomic, metabolic cell modeling system.

Modeling approaches to the dynamics of a living cell are presented that are strongly based on its underlying physical and chemical processes and its hierarchical spatio-temporal organization. Through the inclusion of a broad spectrum of processes and a rigorous analysis of the multiple scale nature of cellular dynamics, we are attempting to advance cell modeling and its applications. The presentation focuses on our cell modeling system, which integrates data archiving and quantitative physico-chemical modeling and information theory to provide a seamless approach to the modeling/data analysis endeavor. Thereby the rapidly growing mess of genomic, proteomic, metabolic, and cell physiological data can be automatically used to develop and calibrate a predictive cell model. The discussion focuses on the Karyote cell modeling system and an introduction to the CellX and VirusX models. The Karyote software system integrates three elements: (1) a model-building and data archiving module that allows one to define a cell type to be modeled through its reaction network, structure, and transport processes as well as to choose the surrounding medium and other parameters of the phenomenon to be modeled; (2) a genomic, proteomic, metabolic cell simulator that solves the equations of metabolic reaction, transcription/translation polymerization and the exchange of molecules between parts of the cell and with the surrounding medium; and (3) an information theory module (ITM) that automates model calibration and development, and integrates a variety of data types with the cell dynamic computations. In Karyote, reactions may be fast (equilibrated) or slow (finite rate), and the special effects of enzymes and other minority species yielding steady-state cycles of arbitrary complexities are accounted for. These features of the dynamics are handled via rigorous multiple scale analysis. A user interface allows for an automated generation and solution of the equations of multiple timescale, compartmented dynamics. Karyote is based on a fixed intracellular structure. However, cell response to changes in the host medium, damage, development or transformation to abnormality can involve dramatic changes in intracellular structure. As this changes the nature of the cellular dynamics, a new model, CellX, is being developed based on the spatial distribution of concentration and other variables. This allows CellX to capture the self-organizing character of cellular behavior. The self-assembly of organelles, viruses, and other subcellular bodies is being addressed in a second new model, VirusX, that integrates molecular mechanics and continuum theory. VirusX is designed to study the influence of a host medium on viral self-assembly, structural stability, infection of a single cell, and transmission of disease.