WHAT FACTORS PROMOTE THE EMERGENCE OF BIOCOMPLEXITY?

1. WHAT FACTORS PROMOTE THE EMERGENCE OF BIOCOMPLEXITY? Robert M. Hazen, Carnegie Institution Kavli Futures Symposium – Bio & Nano June 13, 2007

2. Four Objectives • Identify emergent steps in life’s origins. • Define a system’s complexity in terms of its function. • Identify factors that promote complexification.

3. PART I: ORIGINSCentral Assumptions The first life forms were carbon-based. Life’s origin was a chemical process that relied on water, air, and rock. The origin of life required a sequence of emergent chemical steps of increasing complexity.

4. What is Emergent Complexity? Emergent phenomena arise from interactions among numerous individual particles, or “agents.”

5. Emergent Phenomena – Life

6. Life’s Origins:Four Emergent Steps • Emergence of biomolecules • Emergence of organized molecular systems • Emergence of self-replicating molecular systems • Emergence of natural selection

7. Why Is It Difficult to Quantify Complexity? Genomic Structural X Behavioral X X

8. Functional Information Hazen et al. (2007) defined functional information (I) as related to the fraction of configurations of a system [F(E)] that achieves a specified degree of function (E): I(E) = -log2[F(E)] where I(E) is measured in bits.

9. PART III: How to Increase I(E) 1. Increase the number of interacting agents. 2. Increase the diversity of interacting agents. 3. Increase selective pressures by environmental cycling

10. Implications of I = -log2[F(E)]:System Size and Diversity Sand Grains Galaxies Ant Colonies The Brain

11. Implications of I = -log2[F(E)]:Cycling and Complexification Cycling of environmental conditions (day-night, wet-dry, high-low tide, hot-cold, freeze-thaw) enhances selection processes and therefore increases both E and I. Kessler & Werner (2003) Science 299, 354.

12. Implications of I = -log2[F(E)]:Cycling and Complexification Each cycle has the potential to add information to the system (e.g., waves, aptamers, reproduction).

13. FUNCTIONAL INFORMATION Jack Szostak, Harvard University Experiments in Molecular Evolution

14. Aptamer Evolution • Create a random RNA pool *1

15. Aptamer Evolution • Random RNA pool • Initiate in vitro selection process *1 *2

16. Aptamer Evolution • Random RNA pool • In vitro process • Wash 15 times to remove nonbinding • strands *1 *2 *3

17. Aptamer Evolution • Random RNA pool • In vitro process • Remove nonbinding • strands • Collect bound RNA strands *1 *2 *3 *4

18. Aptamer Evolution • Random RNA pool • In vitro process • Remove nonbinding • strands • Collect bound RNA • Reverse (RNADNA) transcriptase to copy bound sequences *1 *2 *5 *3 *4

19. Aptamer Evolution • Random RNA pool • In vitro process • Remove nonbinding • strands • Collect bound RNA • Reverse transcriptase • Use PCR to amplify bound sequences with errors. *1 *6 *2 *5 *3 *4

20. Aptamer Evolution • Random RNA pool • In vitro process • Remove nonbinding • strands • Collect bound RNA • Reverse transcriptase • PCR amplify with errors • Transcribe DNA to new RNA strands *1 *7 *6 *2 *5 *3 *4

21. Aptamer Evolution • Random RNA pool • In vitro process • Remove nonbinding • strands • Collect bound RNA • Reverse transcriptase • PCR amplify with errors • Transcribe DNA to new RNA strands • Repeat 1 thru 7 *1 *7 *6 *2 *5 *3 *4

22. Results: An RNA molecule which can: • Self replicate • Bind to a non-nucleic acid substrate (BIE) • Perform a chemical reaction ( N-C bonding; i.e.: N-alkylation) • Closely resembles tRNA

23. “ISLANDS OF FITNESS” We propose that the gaps are the result of “islands” of solutions in configuration space.

24. CONCLUSIONS 1. The origin of life required a sequence of emergent steps. 2. Complexity only has meaning in the context of function. 3. We can achieve complexity through design or selection.

25. With thanks to: NASA Astrobiology Institute National Science Foundation Carnegie Institution of Washington