Computer evaluation of network dynamics models with application to cell cycle control in budding yeast

Model Organisms for Studying the Cell Cycle

Regulation of the cell-division cycle is fundamental for the growth, development, and reproduction of all species of life. In the past several decades, a conserved theme of cell cycle regulation has emerged from research in diverse model organisms. A comparison of distinct features of several diverse model organisms commonly used in cell cycle studies highlights their suitability for various experimental approaches, and recaptures their contributions to our current understanding of the eukaryotic cell cycle. A historic perspective presents a recollection of the breakthrough upon unfolding the universal principles of cell cycle control by scientists working with diverse model organisms, thereby appreciating the discovery pathways in this field. A comprehensive understanding is necessary to address current challenging questions about cell cycle control. Advances in genomics, proteomics, quantitative methodologies, and approaches of systems biology are redefining the traditional concept of what constitutes a model organism and have established a new era for development of novel, and refinement of the established model organisms. Researchers working in the field are no longer separated by their favorite model organisms; they have become more integrated into a larger community for gaining greater insights into how a cell divides and cycles. The new technologies provide a broad evolutionary spectrum of the cell-division cycle and allow informative comparisons among different species at a level that has never been possible, exerting unimaginable impact on our comprehensive understanding of cell cycle regulation.

Molecular systems biology of Sic1 in yeast cell cycle regulation through multiscale modeling

Cell cycle control is highly regulated to guarantee the precise timing of events essential for cell growth, i.e., DNA replication onset and cell division. Failure of this control plays a role in cancer and molecules called cyclin-dependent kinase (Cdk) inhibitors (Ckis) exploit a critical function in cell cycle timing. Here we present a multiscale modeling where experimental and computational studies have been employed to investigate structure, function and temporal dynamics of the Cki Sic1 that regulates cell cycle progression in Saccharomyces cerevisiae. Structural analyses reveal molecular details of the interaction between Sic1 and Cdk/cyclin complexes, and biochemical investigation reveals Sic1 function in analogy to its human counterpart p27(Kip1), whose deregulation leads to failure in timing of kinase activation and, therefore, to cancer. Following these findings, a bottom-up systems biology approach has been developed to characterize modular networks addressing Sic1 regulatory function. Through complementary experimentation and modeling, we suggest a mechanism that underlies Sic1 function in controlling temporal waves of cyclins to ensure correct timing of the phase-specific Cdk activities.

A low number of SIC1 mRNA molecules ensures a low noise level in cell cycle progression of budding yeast

The budding yeast genome comprises roughly 6000 genes generating a number of about 10 000 mRNA copies, which gives a general estimation of 1-2 mRNA copies generated per gene. What does this observation implicate for cellular processes and their regulation? Whether the number of mRNA molecules produced is important for setting the amount of proteins implicated in a particular function is at present unknown. In this context, we studied cell cycle control as one of the highly fine tuned processes that guarantee the precise timing of events essential for cell growth. Here, we developed a stochastic model that addresses the effect of varying the mRNA amount of Sic1, inhibitor of the Cdk1-Clb5 kinase activity, and the resulting noise on Sic1/Clb5 balance at the G1/S transition. We considered a range of SIC1 transcripts number according to our experimental data derived from the MS2 mRNA tagging system. Computational simulation revealed that an increased amount of SIC1 mRNAs lead to an amplified dispersion of Sic1 protein levels, suggesting mRNA control being critical to set timing of Sic1 downregulation and, therefore, S phase onset. Moreover, Sic1/Clb5 balance is strongly influenced by Clb5 production in both daughter and mother cells in order to maintain the characteristic time of S phase entry overall the population. Furthermore, CLB5 mRNA molecules calculated to reproduce temporal dynamics of Sic1 and Clb5 for daughter and mother cells agree with recent data obtained from more complex networks. Thus, the results presented here provide novel insights into the influence that the mRNA amount and, indirectly, the transcription process exploit on cell cycle progression.

Systems biology of the cell cycle of Saccharomyces cerevisiae: From network mining to system-level properties

Following a brief description of the operational procedures of systems biology (SB), the cell cycle of budding yeast is discussed as a successful example of a top-down SB analysis. After the reconstruction of the steps that have led to the identification of a sizer plus timer network in the G1 to S transition, it is shown that basic functions of the cell cycle (the setting of the critical cell size and the accuracy of DNA replication) are system-level properties, detected only by integrating molecular analysis with modelling and simulation of their underlying networks. A detailed network structure of a second relevant regulatory step of the cell cycle, the exit from mitosis, derived from extensive data mining, is constructed and discussed. To reach a quantitative understanding of how nutrients control, through signalling, metabolism and transcription, cell growth and cycle is a very relevant aim of SB. Since we know that about 900 gene products are required for cell cycle execution and control in budding yeast, it is quite clear that a purely systematic approach would require too much time. Therefore lines for a modular SB approach, which prioritises molecular and computational investigations for faster cell cycle understanding, are proposed. The relevance of the insight coming from the cell cycle SB studies in developing a new framework for tackling very complex biological processes, such as cancer and aging, is discussed.