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T.M. Murali, Virginia Tech
The complexity, diversity, and richness of experimental data on cellular systems are inspiring the development of computational analysis techniques that can directly prioritize and suggest new experiments.

1. A.R. Joyce and B.O. Palsson, "The Model Organism as a System: Integrating 'Omics' Data Sets," Nature Reviews Molecular Cell Biology, vol. 7, no. 3, 2006, pp. 198-210.
2. Y. Guan et al., "Systematic Planning of Genome-Scale Experiments in Poorly Studied Species," PLoS Computational Biology, vol. 6, no. 3.
3. R.D. King et al., "The Automation of Science," Science, Apr. 2009, vol. 324, no. 5923, pp. 85-89.
4. E. Szczurek et al., "Elucidating Regulatory Mechanisms Downstream of a Signaling Pathway Using Informative Experiments," Molecular Systems Biology, vol. 5, article no. 287.
5. C.H. Yeang et al., "Validation and Refinement of Gene-Regulatory Pathways on a Network of Physical Interactions," Genome Biology, vol. 6, no. 7, 2005, R62+.
6. S. Navlakha and Z. Bar-Joseph, "Algorithms in Nature: The Convergence of Systems Biology and Computational Thinking," Molecular Systems Biology, vol. 7, article no. 546.

Index Terms:
computational biology
Citation:
T.M. Murali, "Guest Editor's Introduction: Computationally Driven Experimental Biology," Computer, vol. 45, no. 3, pp. 22-23, March 2012, doi:10.1109/MC.2012.93
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