Current Genomics, Vol. 5, No. 8, 2004
Contents
Yeast
Systems Biology Special Issue
Guest
Editor: Hiroaki Kitano
Probing Control Mechanisms of Cell Cycle and Ageing in
Budding Yeast Pp. 615-627
L. Alberghina, M. Vai and M. Vanoni
Mass Spectrometry-Based Proteomics for Quantitative Description
of Cellular Events Pp. 629-635
Keiji Kito and Takashi Ito
Shutting the MAP Off – and On Again? Pp. 637-647
Edda Klipp, Bodil Nordlander, Bente Kofahl and Stefan
Hohmann
A Systems Biology Study of Two Distinct Growth Phases of Saccharomyces
cerevisiae Cultures Pp. 649-663
A. M. Martins, D. Camacho, J. Shuman, W. Sha, P. Mendes and V. Shulaev
On the Temporal Self-Organisation of Saccharomyces
cerevisae Pp. 665-671
Douglas B. Murray
Enhancing Yeast Transcription Analysis Through
Integration of Heterogeneous Data Pp. 673-686
T. Grotkjær and J. Nielsen
The Silicon Cell Initiative Pp. 687-697
Jacky L. Snoep and Hans V. Westerhoff
Abstracts
[Back to top]
Probing Control Mechanisms of Cell Cycle and Ageing in Budding Yeast
L. Alberghina, M. Vai and M. Vanoni
A modular systems biology approach may be useful to gain a better understanding of complex cellular processes, such as cell cycle and ageing. We show in fact in this review that this approach has been successfully applied to the identification of the long sought molecular mechanism able to set the critical cell size required to enter S phase in budding yeast. It involves two sequential thresholds set by the cyclin dependent kinase inhibitors Far1 and Sic1, that cooperate in carbon source modulation of the critical cell size required to enter S phase, a hallmark of response of the cell cycle to changing growth conditions. After this initial validation, the approach is tested to extract from available literature data a blueprint of ageing in budding yeast. The blueprint newly proposes that the process of ageing is initiated at the level of the yeast cell wall, due to the increase in size of ageing mother cells. This event would result in a mechanical stress of the cell wall that generates a signaling response able to activate two interconnected major cellular responses involved in ageing: stress metabolism and chromatin remodelling. The ensuing new approach to ageing studies is described in comparison with current theories of cell ageing.
[Back to top]
Mass Spectrometry-Based Proteomics for Quantitative Description of Cellular
Events
Keiji Kito and Takashi Ito
[Back to top] Shutting
the MAP Off – and On Again?
Edda Klipp, Bodil Nordlander, Bente Kofahl and Stefan
Hohmann
Signal transduction pathways are the cellular information routes with which cells monitor their surrounding as well as their own state and adjust to environmental changes or hormonal stimuli. MAP kinase pathways are one type of signalling systems in eukaryotes that control stress responses, cell growth and proliferation as well as differentiation. In this study we compare two very well studied yeast signalling systems, the pheromone response pathway and the osmosensing HOG pathway. We have recently generated mathematical models that allow in silico analysis of signalling properties for both pathways. Deactivation of signalling is as important as activation because inappropriate pathway activation causes cell cycle arrest (in the cases studied here) or uncontrolled proliferation. Both pathways are transiently activated by their stimulus, i.e. mating pheromone and osmostress, respectively, indicating rigorous feedback mechanisms. However, the HOG pathway can readily be reactivated by a subsequent stimulus and this is important for its biological role in mediating osmoadaptation. The pheromone response pathway, however, is desensitised and is unable to respond for a certain period of time. While some mechanisms of feedback control are similar in both systems (such as the downregulatory role of protein phosphatases) the main difference seems to lie in the control of the sensors/receptors. The pheromone receptors are internalised and degraded following stimulation and hence are not available for further stimulation. The osmosensors on the other hand, seem to toggle between activated and deactivated state only controlled by osmotic changes. Together with subtle control by protein phosphatases this results in a system that is constantly receptive for stimulation.
[Back
to top] A Systems Biology Study of Two Distinct
Growth Phases of Saccharomyces cerevisiae Cultures
A. M. Martins, D. Camacho, J. Shuman, W. Sha, P. Mendes
and V. Shulaev
Saccharomyces cerevisiae cultures growing exponentially and after starvation are distinctly different, as shown by several studies at the physiological, biochemical, and morphological levels. One group of studies attempted to be mechanistic, characterizing a few molecules and interactions, while another focused on global observations but remained descriptive or at best phenomenological. Recent advances in large-scale molecular profiling technologies, theoretical, and computational biology, are making possible integrative studies of biological systems, where global observations are explained through computational models with solid theoretical bases. A case study of the systems biology approach applied to the characterization of baker’s yeast cultures in exponential growth and post-diauxic phases is presented.
Twenty cell cultures of S. cerevisiae were grown under similar environmental conditions. Samples from ten of these cultures were collected 11 hours after inoculation, while samples from the other ten were collected 4 days after inoculation. These samples were analyzed for their RNA and metabolite composition using Affymetrix chips and gas chromatography-mass spectrometry (GC-MS). The data were interpreted with statistical analyses and through the use of computer simulations of a kinetic model that was built by merging two independent models of glycolysis and glycerol biosynthesis. The simulation results agree with the exponential growth phase data, while no model is available for the post-diauxic phase. We discuss the need for expanding the number of kinetic models of S. cerevisiae, combining metabolism and genetic regulation, and covering all of its biochemistry.
[Back
to top] On the Temporal Self-Organisation of Saccharomyces
cerevisae
Douglas B. Murray
The budding yeast Saccharomyces cerevisiae has been one of the premier models for the study of eukaryote molecular biology for over 50 years. These studies have revealed a complex and robust yeast phenome and elucidated many of the underlying principles common to all eukaryotes including DNA, RNA, protein and metabolite interaction networks. However the degree complexity and integration of the cellular network has made dissecting the temporal dynamics of the phenotype a rather daunting task. Here I review work on glycolytic oscillation, oscillation observed in continuous culture and colony pattern formation, and find that redox is a central thread underpinning these phenomena. The outputs of the systems involve sub-networks that are at the core of the cellular network, e.g., glycolysis, stress response, respiration, cell cycle, amino acid biosynthesis, leading to the conclusion that a fundamental redox attractor underpins these core cellular processes.
[Back
to top] Enhancing Yeast Transcription Analysis Through Integration of
Heterogeneous Data
T. Grotkjær and J. Nielsen
DNA microarray technology enables the simultaneous measurement of the transcript level of thousands of genes. Primary analysis can be done with basic statistical tools and cluster analysis, but effective and in depth analysis of the vast amount of transcription data requires integration with data from several heterogeneous data sources, such as upstream promoter sequences, genome-scale metabolic models, annotation databases and other experimental data. In this review, we discuss how experimental design, normalisation, heterogeneous data and mathematical modelling can enhance analysis of Saccharomyces cerevisiae whole genome transcription data. A special focus is on the quantitative aspects of normalisation and mathematical modelling approaches, since they are expected to play an increasing role in future DNA microarray analysis studies. Data analysis is exemplified with cluster analysis, and newly developed co-clustering methods, where the DNA microarray analysis is enhanced by integrating data from multiple, heterogeneous sources.
[Back
to top] The Silicon Cell Initiative
Jacky L. Snoep and Hans V. Westerhoff
In the post genomic era and with the rapid developments in the field of Systems Biology there is an increased demand for ‘virtual cells’. Here a number of initiatives towards such cells are discussed with an emphasis on how they differ in aims and functionalities. Then one of the initiatives is described in much more detail. This ‘silicon cell’ initiative aims at producing computer replica of living organisms, with an initial focus on individual cells and pathways therein. Once journal-refereed, the replica are put onto a dedicated website, such that they can be interrogated by the worldwide scientific community. The replicas enable experimentation in silico, which may advance drug design, biotechnology and teaching. A major challenge will be the quality control and improvement, and the coupling of the replica of parts of living cells into replica of larger parts.