Integrative analysis of cell cycle control in budding yeast

Monitoring dynamics of single-cell gene expression over multiple cell cycles

Recent progress in reconstructing gene regulatory networks has established a framework for a quantitative description of the dynamics of many important cellular processes. Such a description will require novel experimental techniques that enable the generation of time-series data for the governing regulatory proteins in a large number of individual living cells. Here, we utilize microfabrication to construct a Tesla microchemostat that permits single-cell fluorescence imaging of gene expression over many cellular generations. The device is used to capture and constrain asymmetrically dividing or motile cells within a trapping region and to deliver nutrients and regulate the cellular population within this region. We illustrate the operation of the microchemostat with Saccharomyces cerevisiae and explore the evolution of single-cell gene expression and cycle time as a function of generation. Our findings highlight the importance of novel assays for quantifying the dynamics of gene expression and cellular growth, and establish a methodology for exploring the effects of gene expression on long-term processes such as cellular aging.

The Yeast Systems Biology Network: mating communities

Systems biology requires model organisms that allow detailed studies to be performed rapidly, accurately and reproducibly. Such model systems are important driving forces for elucidating the principle properties of biomolecules, for developing general concepts for the quantitative description of living cells, and for generating the computational tools for systems analyses. The yeast Saccharomyces cerevisiae serves as an excellent model system. To coordinate efforts and to bring the yeast research and systems biology communities together, the Yeast Systems Biology Network has recently been started.

Mode locking the cell cycle

A characteristic property of nonlinear oscillatory systems is their ability to mode lock to a periodic, external, driving signal. In an n:m mode-locked state, the driven system executes n oscillations to every m oscillations of the driving signal, with a constant phase relationship between the two oscillations. We investigate mode locking for a mathematical model of the cell cycle in budding yeast. We determine which variables are most effective in coupling an external stimulus to the cell cycle oscillator, and speculate about whether experiments are feasible and informative for this model organism.

The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level

The Silicon Cell initiative aims to understand cellular systems on the basis of the characteristics of their components. As a tool to achieve this, detailed kinetic models at the network reaction level are being constructed. Such detailed kinetic models are extremely useful for medical and biotechnological applications and form strong tools for fundamental studies. Several recently constructed detailed kinetic models on metabolism (glycolysis), signal transduction (EGF receptor), and the eukaryotic cell cycle (Saccharomyces cerevisiae) have been used to exemplify the Silicon Cell project. These models are stored and made accessible via the JWS Online Cellular Systems Modeling project, a web-based repository of kinetic models. Using a web-browser the models can be interrogated via a user-friendly graphical interface. The goal of the two projects is to combine models on parts of cellular systems and ultimately to construct detailed kinetic models at the cellular level.

Intracellular Signaling: Spatial and Temporal Control

Cells integrate many inputs through complex networks of interacting signaling pathways. Systems approaches as well as computer-aided reductionist approaches attempt to “untangle the wires” and gain an intimate understanding of cells. But “understanding” any system is just the way that the human mind gains the ability to predict behavior. Computer simulations are an alternative way to achieve this goal—quite possibly the only way for complex systems. We have new tools to probe large sets of unknown interactions, and we have amassed enough detailed information to quantitatively describe many functional modules. Cell physiology has passed the threshold: the time to begin modeling is now.

Cell cycle goes global

The cell division cycle is one of the most intensively studied biological processes, yet, in spite of great effort, many questions remain as to how the cell cycle is controlled by cyclin-dependent kinases and other critical regulators. Recent functional genomic and proteomic approaches have yielded new insights into almost all aspects of cell cycle control, including transcriptional circuits, DNA replication, sister chromatid separation and regulation by environmental signals. Perhaps most notably, systematic analysis has begin to reveal meta-level connections between previously distinct sub-processes. As the interconnections between these huge datasets are beyond intuition, mathematical representation and automated analysis of functional genomic data is an urgent mandate.

Quantitative characterization of a mitotic cyclin threshold regulating exit from mitosis

Regulation of cyclin abundance is central to eukaryotic cell cycle control. Strong overexpression of mitotic cyclins is known to lock the system in mitosis, but the quantitative behavior of the control system as this threshold is approached has only been characterized in the in vitro Xenopus extract system. Here, we quantitate the threshold for mitotic block in budding yeast caused by constitutive overexpression of the mitotic cyclin Clb2. Near this threshold, the system displays marked loss of robustness, in that loss or even heterozygosity for some regulators becomes deleterious or lethal, even though complete loss of these regulators is tolerated at normal cyclin expression levels. Recently, we presented a quantitative kinetic model of the budding yeast cell cycle. Here, we use this model to generate biochemical predictions for Clb2 levels, asynchronous as well as through the cell cycle, as the Clb2 overexpression threshold is approached. The model predictions compare well with biochemical data, even though no data of this type were available during model generation. The loss of robustness of the Clb2 overexpressing system is also predicted by the model. These results provide strong confirmation of the model's predictive ability.

Hysteresis and cell cycle transitions: how crucial is it?

Recently, experiments have shown that cyclin-dependent kinase (CDK) activity exhibits hysteresis in its response to total cyclin when cyclin is made nondegradable and controlled externally. This observation was taken to support mathematical modeling predictions regarding the underlying dynamics of the cell cycle. However, cell cycle dynamics can also be generated by other nonhysteretic mechanisms. To examine the robustness of the hysteretic response of CDK activity to total cyclin, we simulated various cell cycle signal transduction networks, and correlated the dynamics to the response function of CDK activity versus total cyclin. By randomly searching the parameter space, we assessed robustness by estimating the frequency of hysteretic versus nonhysteretic dynamical mechanisms. When the dynamical instabilities were caused by feedback loops in CDK phosphorylation and dephosphorylation or by feedback between cyclin and the CDK inhibitor, the response function of CDK activity versus total cyclin correlated well with the dynamical instabilities. However, when the dynamical instabilities originated from feedback between cyclin and APC-CDH1 or RB-E2F, the response function did not correlate with dynamical instabilities. Thus, although a hysteretic response is neither necessary nor sufficient, it is in general a much more robust mechanism for generating cell cycle dynamics than nonhysteretic mechanisms.

Biological robustness

Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.