Guang Song
Assistant Professor

Office: 107 Atanasoff Phone: (515) 294-1696 Fax: (515) 294-0258 Email: gsong at cs.iastate.edu (replace " at " with "@")

Current Affiliations

• Assistant professor, Department of Computer Science, Iowa State University
• Faculty member, Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University
• Faculty member, Bioinformatics and Computational Biology (BCB) Graduate Program, Iowa State University

Biographical Sketch

Guang Song is an assistant professor in the Department of Computer Science at Iowa State University. He studied theoretical physics for his bachelor's and master's degrees, working on computer modeling and simulation of relativistic heavy-ions collisions. The basic idea is similar to using molecular dynamics (MD) to study the dynamics and functions of biomolecules. He then moved to the field of computer science, studying robotics and motion planning. He soon applied the techniques well developed in robotics to protein folding. His doctoral work (advisor: Nancy M. Amato) on using robotic motion planning to protein folding was novel and was featured in Genome Technology (Nov. 2001 issue) and NSF CISE quarterly newsletter (Sept. 2002) and cited by BBC World (Aug. 2002). His postdoctoral work (advisor: Robert L. Jernigan, 2003-2006) focused on protein motion, dynamics and functions.

Research Interests

Guang Song's research interests spread over several disciplines: physics, robotics, computer science, and biology. One goal of his research is to gain a deeper understanding of life and its fascinating mechanisms, which orchestrate various components to function together. His current research focuses on understanding the mechanisms of protein functions, for example, how does the structure of a protein facilitate the realization of its functions? How does the binding a ligand open up an ion channel at a remote site? His work thus involves physically and structurally based modelings and simulations.
His research interests also include motion planning, robotics, quantum computing, virtual reality, and haptic input.

Research Areas

Bioinformatics and Computational Biology, Robotics, Computational Geometry, Algorithms

Education

Ph.D.   Computer Science, Texas A&M University   2003
M.S.   Physics, Texas A&M University   1998
B.S.   Physics, Jilin University, China   1992

Honors and Awards

COMPAS Junior Faculty Award  Department of Computer Science, Iowa State University, 2007

IBM Research PhD Fellowship  IBM, 2002-2003

Anton Philips Best Student Paper Award Finalist  IEEE International Conference on Robotics and Automation (ICRA), 2001

Representative Publications

Refereed Journal and Conference Publications

Andrzej Kloczkowski, Robert L. Jernigan, Zhijun Wu, Guang Song, Lei Yang, Andrzej Kolinski and Piotr Pokarowski. Distance Matrix-Based Approach to Protein Structure Prediction. Journal of Structural and Functional Genomics. Vol. 10. pp. 67-81, 2009.

Tu-Liang Lin and Guang Song. Predicting Allosteric Communication Pathways Using Motion Correlation Network. 7th Asia Pacific Bioinformatics Conference (APBC), Beijing, China, 2009.

Lei Yang, Guang Song, and Robert L. Jernigan. Comparison of Experimental and Computed Protein Anisotropic Temperature Factors. Proteins, Accepted, 2008.

Lei Yang, Guang Song, Alicia Carriquiry and Robert L. Jernigan. Close Correspondence between the Essential Protein Motions from Principal Component Analysis of Multiple HIV-1 Protease Structures and Elastic Network Modes. Structure, Cell Press. Vol. 16. No. 2. pp. 321-330, 2008.

Guang Song and Robert L. Jernigan. vGNM: a Better Model for Understanding the Dynamics of Proteins in Crystals. Journal of Molecular Biology. Vol. 369. No. 3. pp. 880-93, 2007.

Lei Yang, Guang Song, and Robert L. Jernigan. How Well Can We Understand Large-Scale Protein Motions Using Normal Modes of Elastic Network Models?. Biophysical Journal. Vol. 93. No. 3. pp. 920-9, 2007.

Lei Yang, Guang Song, and Robert L. Jernigan. Comparison of Experimental and Computed Protein Anisotropic Temperature Factors. IEEE BIBM Workshop on Computational Structural Bioinformatics, Silicon Valley, USA, 2007.

Guang Song and Robert L. Jernigan. An Enhanced Elastic Network Model to Represent the Motions of Domain-Swapped Proteins. Proteins. Vol. 63. No. 1. pp. 197-209, 2006.

Shawna Thomas, Guang Song, and Nancy M. Amato. Protein folding by motion planning. Physical Biology. Vol. 2. No. 4. pp. S148-55, 2005.

Guang Song and Andreas Klappenecker. Optimal Realizations of Simplified Toffoli Gates. Journal of Quantum Information and Computation. Vol. 4. No. 5. pp. 361-372, 2004.

Guang Song and Nancy M. Amato. A Motion Planning Approach to Folding: From Paper Craft to Protein Folding. IEEE Transactions on Robotics and Automation. Vol. 20. No. 1. pp. 60-71, 2004.

Nancy M. Amato, Ken A. Dill, and Guang Song. Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures. Journal of Computational Biology. Vol. 10. No. 3-4. pp. 239-256, 2003.

Guang Song and Andreas Klappenecker. Optimal Realizations of Controlled Unitary Gates. Journal of Quantum Information and Computation. Vol. 3. No. 2. pp. 139-155, 2003.

Guang Song and Nancy M. Amato. Using Motion Planning to Study Protein Folding Pathways. the 5th ACM International Conference on Computational Molecular Biology (RECOMB), Montreal, Canada. pp. 287-296, 2001.

O. Burchan Bayazit, Guang Song, Nancy M. Amato. Enhancing Randomized Motion Planners: Exploring with Haptic Hints. Autonomous Robots. Vol. 10. No. 2. pp. 163-174, 2001.

Guang Song and Nancy M. Amato. Randomized Motion Planning for Car-like Robots with C-PRM. International Conference on Intelligent Robots and Systems (IROS), Hawaii, U.S.A.. pp. 37-42, 2001.

Guang Song, Bao-An Li, Che-Ming Ko. Antikaon Production and Medium Effects in Heavy-Ion Collisions at AGS. Nuclear Physics A. Vol. 646. No. 4. pp. 481-499, 1999.

Book Chapters

Robert L. Jernigan, Lei Yang, Ozge Kurkcuoglu, Guang Song, and Pemra Doruker. Elastic Network Models of Coarse-Grained Proteins Are Effective for Studying the Structural Control Exerted over Their Dynamics. In: Coarse-Graining of Condensed Phase and Biomolecular Systems (Ed. Gregory A. Voth), Chapman and Hall/CRC Press, Taylor and Francis Group, 2007.