Paul Nurse, 1996

Paul Nurse. The central role of a CDK in controlling the fission yeast cell cycle Posted with permissionhttps://digitalcommons.rockefeller.edu/harvey-lectures/1064/thumbnail.jp

Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer, faithfully reproduces the known sequence of regulatory activity patterns along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.Comment: 10 pages, 3 figure

Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle

The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis--Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10--15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS300 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

DEVELOPING A MATHEMATICAL MODEL FOR THE FISSION YEAST CELL CYCLE: SIMULATING MUTANTS OVEREXPRESSING EITHER CDC25 OR WEE1

During the last decade several mathematical models were constructed to describe the fission yeast cell cycle. In these models, fluctuations of MPF activity were responsible for cell cycle transitions, and they successfully explained the behaviour of wild-type fission yeast cells and many cell division cycle mutants as well. However, the mutants involved in these models were mainly loss-of-function mutants (either temperature-sensitive point mutants or gene deletion ones). By contrast, the phenotypes of several gene overproducing (op) mutants have been published during the last twenty years, like those of cdc25op and wee1op cells (in the case of the latter one, even the effects of different overexpression levels are known). Since Wee1 and Cdc25 is a kinase-phosphatase pair, regulating MPF activity and as a consequence, timing mitotic onset in fission yeast, a detailed mathematical model of the fission yeast cell cycle should be able to simulate these overexpression mutants. Within the framework of this paper, a formerly published model was tested for these mutants. In order to describe properly the behaviour of cdc25op and wee1op mutants, some alterations had to be made in the original model, both in the parameter values and in the equations. If these corrections have been involved, the newly developed model also maintained its capability to explain the phenotypes of all those mutants, for which the original model was made. Furthermore, the model predicts the phenotypes of two mutants not yet constructed by geneticists

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

BackgroundInteractions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.ResultsThrough simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes.ConclusionsIn general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component

A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle

We have carried out a systems-level analysis of the spatial and temporal dynamics of cell cycle regulators in the fission yeast Schizosaccharomyces pombe. In a comprehensive single-cell analysis, we have precisely quantified the levels of 38 proteins previously identified as regulators of the G2 to mitosis transition and of 7 proteins acting at the G1- to S-phase transition. Only 2 of the 38 mitotic regulators exhibit changes in concentration at the whole-cell level: the mitotic B-type cyclin Cdc13, which accumulates continually throughout the cell cycle, and the regulatory phosphatase Cdc25, which exhibits a complex cell cycle pattern. Both proteins show similar patterns of change within the nucleus as in the whole cell but at higher concentrations. In addition, the concentrations of the major fission yeast cyclin-dependent kinase (CDK) Cdc2, the CDK regulator Suc1, and the inhibitory kinase Wee1 also increase in the nucleus, peaking at mitotic onset, but are constant in the whole cell. The significant increase in concentration with size for Cdc13 supports the view that mitotic B-type cyclin accumulation could act as a cell size sensor. We propose a two-step process for the control of mitosis. First, Cdc13 accumulates in a size-dependent manner, which drives increasing CDK activity. Second, from mid-G2, the increasing nuclear accumulation of Cdc25 and the counteracting Wee1 introduce a bistability switch that results in a rapid rise of CDK activity at the end of G2 and thus, brings about an orderly progression into mitosis