Mathematical biology

The Princeton Companion to Mathematics

Volumn , Issue , 2010, Pages 837-848

  Reed, Michael C ^a  

^a DUKE UNIVERSITY   (United States)

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EID: 72749099954     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (12)

1. Durrett, R., and S. Levin. 1994. The importance of being discrete (and spatial). Theoretical Population Biology 46: 363-94.
2. Eddy, D. M. 1980. Screening for Cancer: Theory, Analysis and Design. Englewood Cliffs, NJ: Prentice-Hall.
3. Fall, C., E. Marland, J. Wagner, and J. Tyson. 2002. Computational Cell Biology. New York: Springer.
4. Fitch, W. M., R. M. Bush, C. A. Bender, and N. J. Cox. 1997. Long term trends in the evolution of H(3) HA1 human influenza type A. Proceedings of the National Academy of Sciences of the United States of America 94:7712-18.
5. Grossberg, S. 1982. Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Boston, MA: Kluwer.
6. Keener, J., and J. Sneyd. 1998. Mathematical Physiology. New York: Springer.
7. Levin, S., B. Grenfell, A. Hastings, and A. Perelson. 1997. Mathematical and computational challenges in population biology and ecosystems science. Science 275:334-43.
8. Nijhout, H. F. 2002. The nature of robustness in development. Bioessays 24(6):553-63.
9. Pevzner, P. A. 2000. Computational Molecular Biology: An Algorithmic Approach. Cambridge, MA: MIT Press.
10. Schlick, T. 2002. Molecular Modeling and Simulation. New York: Springer.
11. Semple, C., and M. Steel. 2003. Phylogenetics. Oxford: Oxford University Press.
12. Waterman, M. S. 1995. Introduction to Computational Biology: Maps, Sequences, and Genomes. London: Chapman and Hall.