Prof. Unger Ron

Full Professor


My research interest is the interface between Biology and Computer Science. We are interested in exploring this interface from two directions: how advanced computational techniques can help to address fundamental biological questions and how biology can inspire new computational approaches. Within this framework, we are 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 system biology. 

1) Studying simple models of protein folding: We are using simple models to address the fundamental questions related to protein folding. Such models capture the essence of the fundamental questions, while being simple enough to enable thorough computational analysis. Using this approach, we have demonstrated the usefulness of genetic algorithms for structure calculations, study the importance of local interactions, analyze protein folding assisted by chaperons, and study long-range interactions in proteins.

2) Studying ncRNA molecules: The importance of short noncoding RNA molecules (ncRNA) in controlling various biological processes became evident in the last several years. Traditional sequence analysis tools are generally not suitable to identify such molecules on a genomic scale. We use a variety of sophisticated computer science methods ranging from suffix trees to SVMs to detect, compare, and characterize ncRNA molecules. This work is done in collaboration with Prof. Shula Michaeli, an experimentalist who is working on understanding the function of ncRNA molecules in parasites.   

3) Molecular Computation:  The exquisite selectivity and specificity of complex protein-based networks suggest that similar principles can be used to devise biological systems that will be able to directly implement any logical circuit as a parallel asynchronous computation. We have 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.

4) System Biology, the robustness of biological systems: We are analyzing genomic networks (like protein - protein interactions, genetic double mutants) in order to understand how biological systems gain their robustness. We are intrigued by the regularity of the overall features of these networks while they consist of so many different underlying components.  Thus, we look for global properties, in addition to being scale-free and local, that may induce robustness.