Mathematics used in Understanding of Health and Disease
Math-based computer models are a powerful tool to discover the details of complex living systems. John Tyson, a professor of biology at Virginia Tech, is the creation of such models to discover how cells process information and make decisions.
“Cells receive information in the form of chemical signals, physical attachment to other cells, or radiation damage, for example,” Tyson said. “Based on this information, cells must give the correct answer as to grow and divide, or to stop growing and repair the damage or to commit suicide.”
The question for a molecular biologist is: What are the underlying molecular mechanisms that implement these systems of information processing? “As a computer is a system of information processing with silicon chips, cables, motherboard, clock and power supply, a cell is a system of information processing made from messenger RNA genes, proteins and enzymes, “Tyson said. “In a way these molecules interact with each other for signs, make decisions and implement the appropriate response.”
Tyson and other biologists want to know how masses of molecules can understand how a cell must respond to their environment to survive, grow and reproduce. “So I do what any good engineer would. We created a mathematical model of the components and their interactions, and let the computer work the details.”
Tyson presented the findings at the American Association for the Advancement of Science meeting Feb. 18-22 in San Diego as part of a session on “Moving through scales: Mathematics for the Investigation of biological hierarchies,” which includes discussions ranging from “HIV interventions in Africa” to “Neural Dynamics of Decision Making”. Tyson talk about the “Network of Molecular Dynamics and Cell Physiology”, or the cell as an information processing system.
The speakers in this session will show how mathematical models help scientists right across scales in biology, and the interaction between sick and healthy for the spread of global pandemic. While such models can inform public health decisions worldwide, Tyson research addresses basic science to the smallest scale – reducing the gap from molecules to cells. “We must first understand the molecular basis of normal cell behavior, then we have the opportunity to learn how the system breaks into diseased cells,” said Tyson.
“What decision processes tell a cell when to grow and divide and when just hang out? Is errors in this decision process to cause cancer. Tumor cells are cells that grow when and where it should not. Cancer is a collection of diseases caused by wrong decisions at the cellular level. The cells no longer follow the rules. We know that the cause is in the molecules is assumed that the application of these rules. ”
During the course of its investigation, Tyson and colleagues have used computer simulations to test their mathematical models. “If the mathematical model of the computer behaves the way cells behave in the laboratory, we gain confidence that we understand the molecular interactions correctly. If not, we can be sure that our models are missing something important.”
Tyson will speak on the control of cell division in yeast and mammalian cells. “The yeast cells are easy to work in the laboratory, and molecular control systems are very similar to the control systems in mammalian cells,” said As a result of the success that Tyson and his colleagues have had on growth models yeast cell division and are now transitioning to mammalian cells and cancer.
“We have still an engineer’s understanding of normal proliferation of mammalian cells and what goes wrong in cancer cells,” said Tyson. “Cancer treatment remains a matter of cutting, blasting, or poisoning cancer cells – and normal cells that stand in the way. We could be more subtle and perhaps more effective in the treatment of cancer if we had the a privileged understanding of systematic molecular networks that control cell growth, division and death, and an ability to manipulate this control system with a new range of drugs and procedures. ”
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