Dr. Altman's primary interests are in the application of computing technologies to basic molecular biological problems, now referred to as bioinformatics. He is particularly interested in the analysis of protein and RNA structure and function, both in an individual problem-centered manner and on a functional genomic scale. He has an interest in applying systems biology concepts to pharmacology and personalized medicine. His core initial work was the development of probabilistic algorithms for the determination of protein structure from sparse and uncertain experimental data. These algorithms have been shown to have some advantages over other methods of structure determination including the ability to calculate not only a protein conformation, but also an explicit estimate of the uncertainty in the position of each atom. His current efforts are in three areas. First, he is interested in techniques for representing biological knowledge (not just data) for automatic scientific computation. His group is working on representing the contents of the scientific literature for pharmacogenomics (http://www.pharmgkb.org/) in order to support the task of building robust models of biological systems. As part of this work, the group is also interested in novel user interfaces to biological data, and computational architectures for knowledge environments. Second, he is interested in the analysis of microenvironments within macromolecules. In particular, his group is developing methods for statistically analyzing related structures to infer the key conserved features that distinguish these structures from unrelated structures. This work has implications for the recognition of functional sites in proteins, for protein engineering, and for drug design (http://feature.stanford.edu/). Finally, he is interested in physics-based simulation of biological structures, particularly RNA and proteins. Within this broad area, our focus has been on creating coarse-grain representations of RNA, and on combining machine learning and informatics methods with simulation to maximize the performance of each (http://simbios.stanford.edu/).
Bio-X Affiliated Faculty