Scientists device a new computational model of the cell

Our bodies, and those of other animals, consist of many cells that all interact with each other in order to acquire the necessary complexity that makes us who we are. This network of cells is incredibly sophisticated, but there is also a lot going on inside an individual cell. Perhaps the insides of a single cell are even more complex than all of them working together. Cells create proteins to perform various functions, and they do so by reading the information present on the genes on our DNA. In a new attempt to uncover the relationships between genes, their output and their associated hierarchy, scientists from the University of California in San Diego created a computer model that automatically gathers this information.

Model

Because the amount of data that can be derived from the DNA is so vast, bioinformatics is becoming an increasingly important field of science. It basically means we use computers to analyze the data, and that is exactly what the researchers in the present study did. While such attempts are not new, their model was generated automatically with a complex algorithm that is able to uncover the hierarchical relationships between genes. Because it finds relationships without scientists looking at the data first, it is potentially able to uncover relationships that we previously did not find before.

Test

After developing their computational model, the scientists tested it on multiple datasets containing genetic information. Their model was able to automatically derive most of the so-called ontological information that was already known. A current, man-made model of the cell contains ten thousands of different hierarchical relationships between genes, which means the aforementioned computational model needs to be made incredibly complex before being able to find them all automatically. 

Outlook

The scientists propose that their way of uncovering relationships between genes and their associated hierarchy potentially gives us more information about so-called gene ontology networks. Because it uncovers these relationships automatically based on known information about how proteins and genes interact, it could potentially lead to a new computational model of the cell, with way more information than what we could have gathered by hand. We could also potentially manipulate it to find out what the exact consequences are of genetic modification.