New “Cell Graphs” Link Tissue Structure to Its Corresponding Biological Function
Computer scientists and biologists in the Data Science Research Center at Rensselaer have
developed a rare collaboration between the two very different fields to pick apart a fundamental roadblock to progress in modern medicine. Their unique partnership has uncovered a new computational model called “cell graphs” that links the structure of human tissue to its corresponding biological function. The tool is a promising step in the effort to bring the power of computational science together with traditional biology in the fight against human diseases such as cancer.
“Previous biological analysis techniques simply ground up tissues and looked at things like gene expression. This is like looking at poll numbers after a vote. You don’t know who voted for what or why, you only know who won the race.”—George Plopper
The ability to understand the complex relationship between cells in our tissues required an entirely new way to do biology, according to the scientists.
“We needed to take biology out of the Petri dish and microscope slide and lift it into a different space. We needed to integrate the interactions in the body on multiple scales,” Yener said.
Yener and his computational counterparts did this by building a computational model using graph theory that linked several different biological data sets from Plopper and his biology counterparts. The biological data came from tissue samples physically removed from the human body and samples grown outside the body in the lab.
The new computational method uses graph theory. Instead of looking at tissue as a series of cells, graph theory simplifies the system into a series of dots and lines with the dots being the cells and the lines their interactions. The program links and compares the accuracy of data from actual human tissue samples from histology with those grown in the controlled laboratory setting. The result is a new tool that can detect and distinguish cancer and quantify the actual differences between different tissues analyzed.
To non-scientists, interactions between two different types of scientists seem like the norm. But, there are no two groups of scientists more disparate than biologists and computer scientists, according to both Yener and Plopper. For biologists, discovery is in the details. How many genes are being expressed in a tissue? What protein is involved in the disease? Computer scientists seek to represent the larger relationship between different data points. What those data points actually represent is largely irrelevant.