Biologist John Tyson, who studies the cell cycle, is a leader in applying mathematical models in molecular cell biology. However, comparing the results of a mathematical model to experimental data is difficult because mathematical results are quantitative (numbers) while much experimental data is qualitative (trends). The mathematical biologist must figure out how to set the numerical values of the â€˜parametersâ€™ in the model equations in order to create an accurate representation of what is going on inside the cell. A simple example is the conversion between Fahrenheit and Celsius temperatures, said mathematician Layne Watson. "You could use several pairs of Fahrenheit and Celsius readings for the same temperature, and try to deduce the formula for converting between the temperature scales."
Previously, Tyson worked with simpler models whose parameters could be determined by trial and error, a process modelers call "parameter twiddling." But he and his coworker, Kathy Chen, wanted to characterize all the protein interactions regulating the cell cycle of budding yeast (the yeast cells familiar to bakers and brewers, and a favorite organism of molecular biologists, as well). "Such fundamental research on the cell cycle of budding yeast provides a basis for understanding the reproduction of human cells and is relevant to the causes and treatment of cancer, to tissue regeneration, and to the control of many pathogens," Tyson said.
For the budding yeast cell cycle, the experimental data consists of observed traits of 130 mutant yeast strains constructed by disabling and/or over-expressing the genes that encode the proteins of the regulatory network. The model has 143 parameters that need to be estimated from the data. "That is a big problem," said Watson. "You can't do that by hand. You can't even do it on a laptop. It takes a supercomputer."
In fact, it required more than 20,000 CPU hours on System X, a 2200 processor parallel computer, using two new algorithms, DIRECT (DIviding RECTangles) and MADS (Mesh Adaptive Direct Search), to estimate the 143 parameters.
"With a tool like this scientists can spend more time working on the model and less time twiddling parameters," said Tyson.
The research is due to appear in 2007 in the Journal of Global Optimization, in the article "Deterministic Parallel Global Parameter Estimation for a Model of the Budding Yeast Cell Cycle," by Thomas D. Panning, Layne T. Watson, Nicholas A. Allen, Katherine C. Chen, Clifford A. Shaffer, and John J. Tyson.
Panning, who is from Tulsa, Okla., received his master of science in computer science in May 2006 and is currently working as a programmer in Germantown, Md. Watson, of Blacksburg, is professor of computer science in the College of Engineering and professor of mathematics in the College of Science. Allen, who is from Columbia, Md., received his Ph.D. in computer science in November 2005 and is now with Microsoft. Chen, of Blacksburg, is a research scientist biological sciences in the College of Science. Shaffer, of Newport, is associate professor of computer science. Tyson, of Blacksburg, is a University Distinguished Professor of biological sciences.
The Virginia Tech computer science team created massively parallel versions of a deterministic global search algorithm, DIRECT, and a deterministic local search algorithm, MADS, to do the twiddling, and then combined the results. "A deterministic global search algorithm systematically explores the parameter space, finding good values," Watson said. "Then the local search algorithm improves the values from the starting points found by the global algorithm."
The parallel computer programs can now be used by others for similar problems. "The parameters found for the budding yeast cell cycle model are good until the next scientist invalidates them with new experimental data. That could be years from now or next week. That's the way science works," says Watson.
Susan Trulove | EurekAlert!
Transport of molecular motors into cilia
28.03.2017 | Aarhus University
Asian dust providing key nutrients for California's giant sequoias
28.03.2017 | University of California - Riverside
The Institute of Semiconductor Technology and the Institute of Physical and Theoretical Chemistry, both members of the Laboratory for Emerging Nanometrology (LENA), at Technische Universität Braunschweig are partners in a new European research project entitled ChipScope, which aims to develop a completely new and extremely small optical microscope capable of observing the interior of living cells in real time. A consortium of 7 partners from 5 countries will tackle this issue with very ambitious objectives during a four-year research program.
To demonstrate the usefulness of this new scientific tool, at the end of the project the developed chip-sized microscope will be used to observe in real-time...
Astronomers from Bonn and Tautenburg in Thuringia (Germany) used the 100-m radio telescope at Effelsberg to observe several galaxy clusters. At the edges of these large accumulations of dark matter, stellar systems (galaxies), hot gas, and charged particles, they found magnetic fields that are exceptionally ordered over distances of many million light years. This makes them the most extended magnetic fields in the universe known so far.
The results will be published on March 22 in the journal „Astronomy & Astrophysics“.
Galaxy clusters are the largest gravitationally bound structures in the universe. With a typical extent of about 10 million light years, i.e. 100 times the...
Researchers at the Goethe University Frankfurt, together with partners from the University of Tübingen in Germany and Queen Mary University as well as Francis Crick Institute from London (UK) have developed a novel technology to decipher the secret ubiquitin code.
Ubiquitin is a small protein that can be linked to other cellular proteins, thereby controlling and modulating their functions. The attachment occurs in many...
In the eternal search for next generation high-efficiency solar cells and LEDs, scientists at Los Alamos National Laboratory and their partners are creating...
Silicon nanosheets are thin, two-dimensional layers with exceptional optoelectronic properties very similar to those of graphene. Albeit, the nanosheets are less stable. Now researchers at the Technical University of Munich (TUM) have, for the first time ever, produced a composite material combining silicon nanosheets and a polymer that is both UV-resistant and easy to process. This brings the scientists a significant step closer to industrial applications like flexible displays and photosensors.
Silicon nanosheets are thin, two-dimensional layers with exceptional optoelectronic properties very similar to those of graphene. Albeit, the nanosheets are...
20.03.2017 | Event News
14.03.2017 | Event News
07.03.2017 | Event News
29.03.2017 | Materials Sciences
29.03.2017 | Physics and Astronomy
29.03.2017 | Earth Sciences