A Hybrid Genetic Algorithm with Pattern Search for finding Heavy Atoms in Protein Crystals
Proceedings of the 7th annual conference on Genetic and evolutionary computation, , 377-384, 2005
Abstract: One approach for determining the molecular structure of proteins is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms, such as mercury or platinum. By comparing measured amplitudes of diffracted x-rays through protein crystals with and without the heavy atoms, the locations of the heavy atoms can be estimated. Once the locations of the heavy atoms are known, the phases of the diffracted x-rays through the protein crystal can be estimated, which in turn enables the structure of the protein to be estimated. Unfortunately, the key step in this process is the estimation of the locations of the heavy atoms, and this is a multi-modal, non-linear inverse problem. We report results of a pilot study that show that a 2-stage hybrid algorithm, using a stochastic genetic algorithm for stage 1 followed by a deterministic pattern search algorithm for stage 2, can successfully locate up to 5 heavy atoms in computer simulated crystals using noise free data. We conclude that the method may be a viable approach for finding heavy atoms in protein crystals, and suggest ways in which the approach can be scaled up to larger problems.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).