No description available.

No description available.

Much of the work on how the cell cycle is regulated has focused on Cyclin-Dependent Kinase (CDK)-mediated regulation of factors that control the coordinate expression of genes required for entry into the cell cycle. In Saccharomyces cerevisiae, SBF and MBF are related transcription factors that co-ordinately activate a large group of genes at the G1/S transition, and their activation depends on the Cln3-Cdk1 form of the cyclin-dependent kinase. However, cells are viable in the absence of Cln3, or SBF and MBF, indicating that other regulatory pathways must exist that activate the budding yeast cell cycle. The known CDK-independent pathways are made up of various phosphatases and plasma membrane transporters that control ion homeostasis in early G1 phase, a time when cells assess environmental growth conditions in order to commit to cell cycle entry. The enigmatic Bck2 protein is thought to act within these CDK-independent pathways, but the means by which it activates G1/S-regulated genes is not known. Bck2 contains little sequence homology to any known protein. In order to understand how CDK-independent pathways operate, I have studied the Bck2 protein using multiple approaches. In one approach, I have screened for novel SBF/MBF-binding proteins in order to determine if other non-CDK proteins, such as Bck2, might activate SBF and MBF. I have also investigated which region of Bck2 is required for its activity in order to determine if Bck2’s transcriptional activation region is essential. Using one of the iii truncation derivatives from this analysis, I have screened for proteins that interact with Bck2. One of these novel proteins is Mcm1, a global transcriptional activator of genes involved in cell cycle progression, mating gene transcription and metabolism. My studies suggest that Bck2 regulates the activity of Mcm1 in early G1 phase to activate the expression of SWI4, CLN3, and others. My evidence suggests that Bck2 competes for binding to a specific pocket on Mcm1 that is also bound by an Mcm1 repressor called Yox1. My findings suggest that CDK-independent pathways function through Bck2, in order to induce the initial suite of genes required for entry into the cell cycle.

Much of the work on how the cell cycle is regulated has focused on Cyclin-Dependent Kinase (CDK)-mediated regulation of factors that control the coordinate expression of genes required for entry into the cell cycle. In Saccharomyces cerevisiae, SBF and MBF are related transcription factors that co-ordinately activate a large group of genes at the G1/S transition, and their activation depends on the Cln3-Cdk1 form of the cyclin-dependent kinase. However, cells are viable in the absence of Cln3, or SBF and MBF, indicating that other regulatory pathways must exist that activate the budding yeast cell cycle. The known CDK-independent pathways are made up of various phosphatases and plasma membrane transporters that control ion homeostasis in early G1 phase, a time when cells assess environmental growth conditions in order to commit to cell cycle entry. The enigmatic Bck2 protein is thought to act within these CDK-independent pathways, but the means by which it activates G1/S-regulated genes is not known. Bck2 contains little sequence homology to any known protein. In order to understand how CDK-independent pathways operate, I have studied the Bck2 protein using multiple approaches. In one approach, I have screened for novel SBF/MBF-binding proteins in order to determine if other non-CDK proteins, such as Bck2, might activate SBF and MBF. I have also investigated which region of Bck2 is required for its activity in order to determine if Bck2’s transcriptional activation region is essential. Using one of the iii truncation derivatives from this analysis, I have screened for proteins that interact with Bck2. One of these novel proteins is Mcm1, a global transcriptional activator of genes involved in cell cycle progression, mating gene transcription and metabolism. My studies suggest that Bck2 regulates the activity of Mcm1 in early G1 phase to activate the expression of SWI4, CLN3, and others. My evidence suggests that Bck2 competes for binding to a specific pocket on Mcm1 that is also bound by an Mcm1 repressor called Yox1. My findings suggest that CDK-independent pathways function through Bck2, in order to induce the initial suite of genes required for entry into the cell cycle.

The cell cycle of budding yeast is controlled by a complex chemically reacting network of a large group of species, including mRNAs and proteins. Many mathematical models have been proposed to unravel its molecular mechanism. However, it is hard for people with less training to visually interpret the dynamics from the simulation results of these models. In this thesis, we use the visualization toolkit D3 and jQuery to design a web-based interface and help users to visualize the cell cycle simulation results. It is essentially a website where the proliferation of the wild-type and mutant cells can be visualized as dynamical animation. With the help of this visualization tool, we can easily and intuitively see many key steps in the budding yeast cell cycle procedure, such as bud emergence, DNA synthesis, mitosis, cell division, and the current populations of species. / Master of Science / The cell cycle of budding yeast is controlled by a complex chemically reacting network. Many mathematical models have been proposed to unravel its molecular mechanism. However, it is hard to visually interpret the dynamics from the simulation results of these models. In this thesis, we use the visualization toolkit D3 and jQuery to design a web-based interface and help users to visualize the cell cycle simulation results. It is essentially a webpage where the proliferation of the wild-type and mutant cells can be visualized as dynamical animation.

It is important to study gene expression and change patterns over a time period because biologically related gene groups are likely to share similar patterns. In this study, similar gene expression and change patterns are found via model-based clustering method. Fourier and wavelet coefficients of gene expression data are used as the clustering variables. A two-stage model-based method is proposed for stepwise clustering of expression and change patterns. Simulation study is performed to investigate the effectiveness of the proposed methodology. Yeast cell cycle data are analyzed.

Mathematical modeling has become increasingly popular as a tool to study regulatory interactions within gene-protein networks. From the modeler's perspective, two challenges arise in the process of building a mathematical model. First, the same regulatory network can be translated into different types of models at different levels of detail, and the modeler must choose an appropriate level to describe the network. Second, realistic regulatory networks are complicated due to the large number of biochemical species and interactions that govern any physiological process. Constructing and validating a realistic mathematical model of such a network can be a difficult and lengthy task. To confront the first challenge, we develop a new modeling approach that classifies components in the networks into three classes of variables, which are described by different rate laws. These three classes serve as "building blocks" that can be connected to build a complex regulatory network. We show that our approach combines the best features of different types of models, and we demonstrate its utility by applying it to the budding yeast cell cycle. To confront the second challenge, modelers have developed rule-based modeling as a framework to build complex mathematical models. In this approach, the modeler describes a set of rules that instructs the computer to automatically generate all possible chemical reactions in the network. Building a mathematical model using rule-based modeling is not only less time-consuming and error-prone, but also allows modelers to account comprehensively for many different mechanistic details of a molecular regulatory system. We demonstrate the potential of rule-based modeling by applying it to the generation of circadian rhythms in cyanobacteria. / Ph. D.