The research, a collaboration between researchers from the Hebrew University, Stanford and Tel Aviv University, was published on May 11 in the prestigious journal Nature Genetics
Avi Blizovsky
Direct link to this page: https://www.hayadan.org.il/huji230503.html
Researchers at the Rachel and Salim Benin Schools of Engineering and Computer Science at the Hebrew University will publish the results of an innovative study this evening about the systematic identification of control mechanisms of intracellular processes. The study, published in the prestigious journal Nature Genetics, was carried out by the Computational Biology Group of Dr. Nir Friedman from the Hebrew University, in close cooperation with biologists and computer scientists from Stanford University and Tel Aviv University.
According to Dr. Nir Friedman, one of the organizers of the study program in computational biology, "This research is a significant step towards the automatic identification of the control mechanisms in the cell with the help of computational tools. I hope that this research will lead to discoveries that will accelerate the development of drugs for diseases caused by damage to the control processes of the cell, such as cancer, which is characterized by uncontrolled division of cells".
The results of the study are a leap forward in data analysis at the whole genome level. Dr. Nir Friedman who collaborates with biologists in the research of lung cancer and multiple sclerosis says that thanks to the new computational method it will be possible to identify the control mechanisms that have broken down in these diseases. This understanding is important for research that tries to find out how to prevent these diseases.
Dr. Friedman explains that "this research is based on a relatively new field - computational biology, or bioinformatics. This field combines the computational sciences, such as mathematics, statistics and computers, with the life sciences to solve biological problems which require the development of advanced computational techniques. The recently completed human genome project is an example of a huge collection of data for which it was necessary to develop such techniques. This innovative field gained momentum In the last decade, due to the development of several technologies for biological research that perform large-scale experimental measurements, and the mass of data they provide must be analyzed on a computer. It must be clarified that manual analysis cannot handle mass The data is enormous, so an advanced and complex calculation tool is needed."
The current research analyzes information from technology that exists today of tiny facilities that allow a sort of snapshot of the genes in the cell. In any given situation, the living cell uses only a small part of the thousands of genes present in it. The existing technology provides in one experiment measurements of the level of use of each and every one of the genes. By performing experiments on yeast cells, which are in different states, it is possible to measure typical usage levels for these states.
Understanding life at the most basic level means characterizing the way the cell organizes the processes that take place in it, such as: identifying the functional units that make up the cellular system, finding the genes that control activation and restraint of each functional unit and the conditions under which this control occurs. To this day, the characterization of the control processes in the cell from the existing technology is considered an unsolved problem.
The innovative approach developed by Dr. Friedman and his colleagues uses developments from the field of computational learning, which focuses on understanding legality and causality from databases. The researchers built a computational system that processes information from the technology that exists today and deduces from it laws regarding which gene is responsible for a certain control process in the cell? What is the control process What happens in the cell? And when does the control process occur in the cell? This is how the computer automatically labels the function of each gene Control, both in terms of the processes for which he is responsible and in terms of the conditions in which he operates.
The unique computational approach allowed the researchers to formulate a large number of biological predictions about the results of future experiments and about genes whose function was unknown. The predictions were verified in several experiments that confirmed the functional labeling of genes whose function was not known until now. These results confirm the computational approach, and allow reliance on it to identify intracellular control mechanisms from gene usage data.
A concentration of life science and medicine experts
https://www.hayadan.org.il/BuildaGate4/general2/data_card.php?Cat=~~~531663759~~~25&SiteName=hayadan
