WHAT FACTORS PROMOTE
THE EMERGENCE
OF BIOCOMPLEXITY?
Robert M. Hazen, Carnegie Institution
Kavli Futures Symposium – Bio & Nano
June 13, 2007
Four Objectives
1.Identify emergent steps in
life’s origins.
2.Define a system’s complexity
in terms of its function.
3.Identify factors that promote
complexification.
PART I: ORIGINS
Central 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.
What is Emergent Complexity?
Emergent phenomena arise from
interactions among numerous individual
particles, or “agents.”
Emergent Phenomena – Life
Life’s Origins:
Four Emergent Steps
1.
2.
Emergence of biomolecules
3.
Emergence of self-replicating
molecular systems
4.
Emergence of natural selection
Emergence of organized
molecular systems
Why Is It Difficult to
Quantify Complexity?
Genomic
Structural
X
Behavioral
X
X
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.
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
Implications of I = -log2[F(E)]:
System Size and Diversity
Sand Grains
Galaxies
Ant Colonies
The Brain
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.
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).
FUNCTIONAL INFORMATION
Jack Szostak, Harvard University
Experiments in Molecular Evolution
Aptamer Evolution
*1
1. Create a random
RNA pool
Aptamer Evolution
*1
*2
1. Random RNA pool
2. Initiate in vitro
selection process
Aptamer Evolution
*1
*2
*3
1. Random RNA pool
2. In vitro process
3. Wash 15 times to
remove nonbinding
strands
Aptamer Evolution
*1
*2
*3
*4
1. Random RNA pool
2. In vitro process
3. Remove nonbinding
strands
4. Collect bound RNA
strands
Aptamer Evolution
*1
*2
*5
*3
*4
1. Random RNA pool
2. In vitro process
3. Remove nonbinding
strands
4. Collect bound RNA
5. Reverse (RNADNA)
transcriptase to copy
bound sequences
Aptamer Evolution
*1
*6
*2
*5
*3
*4
1. Random RNA pool
2. In vitro process
3. Remove nonbinding
strands
4. Collect bound RNA
5. Reverse transcriptase
6. Use PCR to amplify
bound sequences with
errors.
Aptamer Evolution
*1
*7
*6
*2
*5
*3
*4
1. Random RNA pool
2. In vitro process
3. Remove nonbinding
strands
4. Collect bound RNA
5. Reverse transcriptase
6. PCR amplify with
errors
7. Transcribe DNA to
new RNA strands
Aptamer Evolution
*1
*7
*6
*2
*5
*3
*4
1. Random RNA pool
2. In vitro process
3. Remove nonbinding
strands
4. Collect bound RNA
5. Reverse transcriptase
6. PCR amplify with
errors
7. Transcribe DNA to
new RNA strands
8. Repeat 1 thru 7
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.: Nalkylation)
•Closely resembles tRNA
“ISLANDS OF FITNESS”
We propose that the gaps are the result of
“islands” of solutions in configuration space.
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.
With thanks to:
NASA Astrobiology Institute
National Science Foundation
Carnegie Institution of Washington