The cycle of cell growth, DNA synthesis, mitosis and cell division is 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 dozens of 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 noisy events in the growth-division cycle. Noise stems 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). The goal of the proposed project is to create a realistic and accurate stochastic model of cell cycle control in budding yeast. To accomplish this goal the investigators will: 1) Formulate the molecular regulatory system in terms of elementary biochemical reactions, suitable for exact stochastic simulation. 2) Employ appropriate methods for approximate simulation of these stochastic process, in order to efficiently compute probabilities suitable for comparison to experiments. 3) Develop methods for parameter estimation, sensitivity analysis and bifurcation theory of stochastic dynamical systems. 4) Create a software/hardware environment that supports the demanding computations required of stochastic models of any realistic gene/mRNA/protein regulatory network. 5) Apply the methods and tools to known variability in growth and division of single yeast cells. The multi-disciplinary team at Virginia Tech has proven expertise in all aspects of the project and will be supported by external advisors who are top researchers in the areas of stochastic simulation, sensitivity analysis, bifurcation theory 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 roles of cell division in basic biological processes of significant relevance to human health: e.g., embyronic development, tissue regeneration, wound healing, and carcinogenesis. ? ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM078989-03
Application #
7436098
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Remington, Karin A
Project Start
2006-06-06
Project End
2010-05-31
Budget Start
2008-06-01
Budget End
2009-05-31
Support Year
3
Fiscal Year
2008
Total Cost
$313,289
Indirect Cost
Name
Virginia Polytechnic Institute and State University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
Novák, Béla; Heldt, Frank Stefan; Tyson, John J (2018) Genome Stability during Cell Proliferation: A Systems Analysis of the Molecular Mechanisms Controlling Progression through the Eukaryotic Cell Cycle. Curr Opin Syst Biol 9:22-31
Hopkins, Michael; Tyson, John J; Novák, Béla (2017) Cell-cycle transitions: a common role for stoichiometric inhibitors. Mol Biol Cell 28:3437-3446
Oguz, Cihan; Watson, Layne T; Baumann, William T et al. (2017) Predicting network modules of cell cycle regulators using relative protein abundance statistics. BMC Syst Biol 11:30
Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T et al. (2016) A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks. PLoS One 11:e0153738
Barik, Debashis; Ball, David A; Peccoud, Jean et al. (2016) A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability. PLoS Comput Biol 12:e1005230
Adames, Neil R; Schuck, P Logan; Chen, Katherine C et al. (2015) Experimental testing of a new integrated model of the budding yeast Start transition. Mol Biol Cell 26:3966-84
Palmisano, Alida; Hoops, Stefan; Watson, Layne T et al. (2015) JigCell Run Manager (JC-RM): a tool for managing large sets of biochemical model parametrizations. BMC Syst Biol 9:95
Wang, Shuo; Cao, Yang (2015) The Abridgment and Relaxation Time for a Linear Multi-Scale Model Based on Multiple Site Phosphorylation. PLoS One 10:e0133295
Gérard, Claude; Tyson, John J; Coudreuse, Damien et al. (2015) Cell cycle control by a minimal Cdk network. PLoS Comput Biol 11:e1004056
Tyson, John J; Novak, Bela (2015) Bistability, oscillations, and traveling waves in frog egg extracts. Bull Math Biol 77:796-816

Showing the most recent 10 out of 50 publications