The cycle of cell growth, DNA synthesis, mitosis and cell division is the fundamental process by which cells (and all living organisms) grow, develop and reproduce. Hence, it is of crucial importance to science and human health to understand the molecular mechanisms that control these processes in eukaryotic cells. The control system is so complex that mathematical and computational methods are needed to reliably track the interactions of all the relevant genes, mRNAs, proteins, and multiprotein complexes. Deterministic models (ordinary differential equations) are adequate for understanding the average behavior of groups of cells, but to understand the far-from-average behavior of individual cells requires stochastic models that accurately account for noise stemming from small numbers of participating molecules within a single cell and from vagaries of the division process (i.e., unequal partitioning of molecular components between daughter cells). Accurately modeling the variable responses among cells in a population may be critical to understanding abnormal and diseased cell proliferation. The goals of the proposed renewal are to 1) develop a realistic and accurate stochastic model of cell cycle control in budding yeast and to extend this model to the control of mammalian cell proliferation, 2) measure stochastic effects in single yeast cells in order to provide experimental constraints on and tests of the model, and 3) develop effective algorithms and software to support stochastic modeling and simulation, and to make these tools readily available to the scientific community. Our multi-disciplinary team at Virginia Tech has proven expertise in all aspects of the project and close collaborations with top researchers in the areas of stochastic simulation, sensitivity analysis, bifurcation theory, modeling software, and yeast genetics. Because all eukaryotic cells seem to employ the same fundamental molecular machinery that regulates the cell cycle of yeast, success in modeling growth and division of single yeast cells will translate into better understanding of the role of mammalian cell division in basic biological processes of significance to human health: e.g., embyronic development, tissue regeneration, wound healing, and carcinogenesis.

Public Health Relevance

The cell division cycle is the fundamental process of biological growth and reproduction, and mistakes in this process underlie many serious health problems, especially cancer. An integrative understanding of the cellular basis of health and disease will require, among other things, a description of the cell cycle by computational models that account accurately for the reliability of DNA replication and inheritance despite the molecular fluctuations that inevitably occur in the small confines of a living cell. Hence, a validated stochastic model of the eukaryotic cell cycle is essential to progress in the field of molecular systems biology.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Lyster, Peter
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Virginia Polytechnic Institute and State University
Schools of Arts and Sciences
United States
Zip Code
Tyson, John J; Novak, Bela (2015) Bistability, oscillations, and traveling waves in frog egg extracts. Bull Math Biol 77:796-816
Peccoud, Jean (2014) If you can't measure it, you can't manage it. PLoS Comput Biol 10:e1003462
Palmisano, Alida; Hoops, Stefan; Watson, Layne T et al. (2014) Multistate Model Builder (MSMB): a flexible editor for compact biochemical models. BMC Syst Biol 8:42
Ball, David A; Lux, Matthew W; Adames, Neil R et al. (2014) Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology. PLoS One 9:e107087
Verdugo, Anael; Vinod, P K; Tyson, John J et al. (2013) Molecular mechanisms creating bistable switches at cell cycle transitions. Open Biol 3:120179
Ball, David A; Adames, Neil R; Reischmann, Nadine et al. (2013) Measurement and modeling of transcriptional noise in the cell cycle regulatory network. Cell Cycle 12:3203-18
Oguz, Cihan; Laomettachit, Teeraphan; Chen, Katherine C et al. (2013) Optimization and model reduction in the high dimensional parameter space of a budding yeast cell cycle model. BMC Syst Biol 7:53
Gerard, Claude; Tyson, John J; Novak, Bela (2013) Minimal models for cell-cycle control based on competitive inhibition and multisite phosphorylations of Cdk substrates. Biophys J 104:1367-79
Liu, Zhen; Pu, Yang; Li, Fei et al. (2012) Hybrid modeling and simulation of stochastic effects on progression through the eukaryotic cell cycle. J Chem Phys 136:034105
Clarke, Robert; Cook, Katherine L; Hu, Rong et al. (2012) Endoplasmic reticulum stress, the unfolded protein response, autophagy, and the integrated regulation of breast cancer cell fate. Cancer Res 72:1321-31

Showing the most recent 10 out of 30 publications