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Virginia Tech Researchers Refine Model to Predict Errors in Cell Division

Friday, October 02, 2015  
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A team of Virginia Tech researchers has refined a mathematical model that simulates the impact of genetic mutations on cell division -- a step that could provide insight into errors that produce and sustain harmful cell populations, such as those found in tumors.

In a recently published article in the journal Molecular Biology of the Cell, the group detailed their findings in laboratory experiments, which examined how mutant cells moved through a series of processes to duplicate their genetic material and divide.

Using these results, the group developed more accurate techniques for predicting the effects that gene mutations will have on a cell's ability to regulate its rate of division using natural checkpoints such as cell size and the availability of nutrients.

"Cell division is an energy-intensive process," said Neil Adames, lead author on the study and a senior research associate at the Virginia Bioinformatics Institute. "Cells need to regulate their sizes in order to take in nutrients from their environment effectively and maintain proper concentrations of molecules, so healthy cells usually won't commit to duplicating their chromosomes until they've reached a sufficient size and mass."

In the new study, the researchers tested predictions about whether yeast cells with particular mutations would be able to survive and reproduce. Inaccurate predictions were used to improve the model and provide a better understanding of the biochemical mechanisms governing cell division.

This "integrative" model is the result of a long-term collaboration between several Virginia Tech researchers, including Jean Peccoud, a professor at the Virginia Bioinformatics Institute; T.M. Murali, a professor of computer science in the College of Engineering; and John Tyson, a University Distinguished Professor of biological sciences in the College of Science.

"Biological experimentation has helped drive our team's production of new algorithms, which are now capable of drawing connections between cellular processes in a matter of seconds," Murali said. "By working together to improve this model, the research team is able to generate and test hypotheses faster than ever before."

This streamlined process could be particularly beneficial to cancer research, where scientists still struggle to assess how certain mutations are able to thrive and reproduce.

With support from the National Institutes of Health grants "Integrating Top-Down and Bottom-Up Models in Systems Biology" (1R01GM095955) and "Stochastic Models of Cell Cycle Regulation in Eukaryotes" (2R01GM078989), the group also hopes to advance understanding of how cell cycle checkpoints can continue to function effectively despite variations in protein concentrations.

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