Prof. Ron Unger is an Associate Professor in the Mina and Everard Goodman Faculty of Life Sciences and the Head of the Computational Biology Study Program. He is also a member of the Nano Medicine Center at the Institute of Nanotechnology and Advanced Materials (BINA).
Unger’s goal is to integrate biologically oriented concepts such as neural networks and genetic algorithms into computer science and to adapt computer science algorithms like pattern matching and computational geometry to address actual biological problems. Within this context, his research team is involved in projects spanning a wide range of topics such as designing simple models to better understand protein structure and protein folding dynamics, studying ncRNA molecules, molecular computation and systems biology.
Unger’s group uses simple models to address the fundamental questions related to protein folding. Such models capture the essence of these issues, while being simple enough to enable thorough computational analysis. Using their approach, they have demonstrated the usefulness of genetic algorithms for structure calculations. They have also shown that local interactions facilitate folding, a conclusion they validated with experimental data about folding rates of small proteins. The group is currently focusing on using simple models and machine learning algorithms to study protein folding in the presence of chaperons.
The importance of short noncoding RNA molecules (ncRNA) in controlling various biological processes has become evident in the last several years. Traditional sequence analysis tools are generally not suitable to identify such molecules on a genomic scale. Unger’s lab team is attempting todetect, compare, and characterize ncRNA moleculesin order to establish the existence of additional families of ncRNA, and to gain a rough estimate of their number in various organisms. In order to accomplish these goals, they use a combination of computational biology research techniques like comparative genomics, dotplot based methods, clustering algorithms, suffix trees and suffix arrays, and visualization tools.
Unlike man-made systems, biological systems were not designed; they evolve to carry out their function. Yet, biological systems exhibit resilience and robustness that in many cases exceed their man-made counterparts. Unger’s lab is investigating, again using simple models, the origin of the robustness of evolving systems. Such studies can help us understand the relationship between small genetic modifications (such as SNPs) and their phenotypic effects, in particular in analyzing multi-factorial diseases.
The exquisite selectivity and specificity of complex protein-based networks suggest that similar principles can be used to devise biological systems able to directly implement any logical circuit as a parallel asynchronous computation. Unger’s team has designed a scheme for protein molecules that would serve as the basic computational element by functioning as a NAND logical gate, utilizing DNA tags for recognition, and phosphorylation and exonuclease reactions for information processing.