March 2011

Why is evolution so hard to understand?

What if animations, such as the spectacular “Inner Life of the Cell” video, presented stochastic realities and eschewed scenes in which molecules appear to know where they are going? Would it help student understanding if depictions of polypeptide synthesis, for example, consistently illustrated the fact that during translation, the ribosome-mRNA complex must reject a substantial number of uncharged and charged but inappropriate aminoacyl-tRNAs before random motion brings the correct aminoacyl-tRNA to the active site? Or that transcription factors use their nonspecific, low-affinity binding to DNA to facilitate interactions with their high-affinity targets via one-dimensional diffusion? Or that regulatory noise plays a key role in how biological systems, from operons to neural networks, work? After all, the lac operon would not function if it were not leaky!

Could the fact that mutations only come in a limited number of generic types (9) and often have relatively mild effects be used to explain how drift and genetic noise can lead to evolutionary innovation in response to selective pressures (10)? In that light, would the inherent instability of DNA (11, 12) and genome dynamics, as illustrated by the prevalence of somatic mutations and copy number variation (13 – 15), make evolution in general, and the origin and evolution of cancer and other diseases in particular, more comprehensible? What if students understood that even simple systems of gene interactions can produce complexand surprising behaviors (16 – 18)?

In each case, the goal of presenting these biological scenarios would be to establish and reinforce the multiple ways that function and biological meaning can arise out of random processes. The goal is to address directly what makes the naturalistic, evolutionary explanation of life possible yet difficult to accept. Of course, the educational approach to helping students understand evolution depends on accessible and well-designed course materials and a commitment to universal learning rather than the sorting of students. Learning how evolution works will require that we provide students with the time and feedback needed to come to accept what are, on their face, implausible ideas.

References

1. Berkman, M.B. and E. Plutzer. (2010). Defeating creationism in the courtroom, but not in the classroom. Science 331, 404 – 405.
2. Gregory, T. R. (2009). Understanding natural selection: essential concepts and common misconceptions. Evo. Edu. Outreach 2, 156 – 175.
3. Klymkowsky, M. W., Underwood, S. M., and Garvin-Doxas, K. (2010). The Biological Concepts Instrument (BCI), a diagnostic tool to reveal student thinking.
4. Garvin-Doxas, K., and Klymkowsky, M. W. (2008). Understanding randomness and its impact on student learning: lessons from the Biology Concept Inventory (BCI). Life Science Education 7, 227 – 233.
5. Ridley, M. (2010). The rational optimist. New York: Harper.
6. Copley, S. D. (2003). Enzymes with extra talents: moonlighting functions and catalytic promiscuity. Curr. Opin. Chem. Biol. 7, 265 – 272.
7. Khersonsky, O., and Tawfik, D. S. (2010). Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 79, 471 – 505.
8. Kim, J., Kershner, J. P., Novikov, Y., Shoemaker, R. K., and Copley, S. D. (2010). Three serendipitous pathways in E. coli can bypass a block in pyridoxal-5'-phosphate synthesis. Mol. Syst. Biol. 6, 436.
9. Muller, H. J. (1932). Further studies on the nature and causes of gene mutations. Sixth Int. Cong. Genet. 1, 213 – 255.
10. Blount, Z. D., Borland, C. Z., and Lenski, R. E. (2008). Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 105, 7899 – 7906.
11. Amosova, O., Coulter, R., and Fresco, J. R. (2006). Self-catalyzed site-specific depurination of guanine residues within gene sequences. Proc. Natl. Acad. Sci. U.S.A. 103, 4392 – 4397.
12. Lindahl, T., and Nyberg, B. (1972). Rate of depurination of native deoxyribonucleic acid. Biochemistry 11, 3610 – 3618.
13. Marques-Bonet, T., et al. (2009). A burst of segmental duplications in the genome of the African great ape ancestor. Nature 457, 877 – 881.
14. Schrider, D. R., and Hahn, M. W. (2010). Gene copy-number polymorphism in nature. Proc. Biol. Sci. 277, 3213 – 3221.
15. Stankiewicz, P., and Lupski, J. R. (2010). Structural variation in the human genome and its role in disease. Annu. Rev. Med. 61, 437 – 455.
16. Atkinson, M. R., Savageau, M. A., Myers, J. T., and Ninfa, A. J. (2003). Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli. Cell 113, 597 – 607.
17. Chang, D. E., Leung, S., Atkinson, M. R., Reifler, A., Forger, D., and Ninfa, A. J. (2010). Building biological memory by linking positive feedback loops. Proc. Natl. Acad. Sci. U.S.A. 107, 175 – 180.
18. Elowitz, M. B., and Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature 403, 335 – 338.

Mike Klymkowsky (michael.klymkowsky@colorado.edu) is a professor of molecular, cellular and developmental biology and co-director of CU Teach at the University of Colorado, Boulder.

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