Bruce Tidor, PhD

Photo of Professor Tidor.



Professor of Biological Engineering and Computer Science


Quantifying uncertainty in biological network models
Molecular design of drug resistance-resistant inhibitors
Creation of a biological calcium sensor by the rational redesign of calmodulin
Understanding oscillatory biological systems using dynamic optimization techniques
Antibody affinity maturation using computational protein design
Information theoretic approaches to identifying informative subsets of biological data
Dynamic optimization as a method to probe biological networks and their behavior


Prof. Tidor completed his Bachelor’s degree in Chemistry and Physics at Harvard College. After a Master of Science degree at the University of Oxford, UK as a Marshall Scholar, he returned to his Alma Mater at Harvard for his doctoral studies in Biophysics. Bruce Tidor spent four years as a Whitehead Fellow at the Whitehead Institute for Biomedical Research. He joined MIT as Assistant Professor of Chemistry in 1994, became Associate Professor of Biological Engineering and Computer Science in 2001, and Professor of Biological Engineering and Computer Science in 2005.


Research in the Tidor Group is focused on the analysis of complex biological systems at the molecular and network levels. Projects at the molecular level study the structure and properties of proteins, nucleic acids, and their complexes. Investigations probe the sources of stability and specificity that drive macromolecular folding, binding, and catalysis. Studies are aimed at dissecting the interactions responsible for the specific structure of folded proteins and the binding geometry of molecular complexes. The roles played by salt bridges, hydrogen bonds, side-chain packing, rotameric states, solvation, and the hydrophobic effect in native biomolecules are being explored, and strategies for re-casting these roles through structure-based molecular design are being developed. Work at the network level involves the study of biochemical regulatory networks and signal transduction pathways in cells. The development of approaches to relate network topology to functional characteristics is fundamental to this research. Significant effort is being applied to extracting the design principles for biological networks and to understanding the control functions implemented. The insights resulting from this work will provide a strong foundation for understanding biological systems; moreover, they will be useful for the development of therapies that ameliorate disease states, as well as for the construction of new synthetic systems from biological components. The methods of theoretical and computational biophysics and approaches from computer science, artificial intelligence, applied mathematics, and chemical and electrical engineering play fundamental roles in this work.


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