what factors promote the emergence of biocomplexity? what factors promote the emergence of...

WHAT FACTORS PROMOTE WHAT FACTORS PROMOTE THE EMERGENCETHE EMERGENCE

OF BIOCOMPLEXITY?OF BIOCOMPLEXITY?

WHAT FACTORS PROMOTE WHAT FACTORS PROMOTE THE EMERGENCETHE EMERGENCE

OF BIOCOMPLEXITY?OF BIOCOMPLEXITY?

Robert M. Hazen, Carnegie InstitutionKavli Futures Symposium – Bio & Nano

June 13, 2007

Four ObjectivesFour ObjectivesFour ObjectivesFour Objectives

1.1.Identify emergent steps in Identify emergent steps in life’s origins.life’s origins.

2.2.Define a system’s complexity Define a system’s complexity in terms of its function.in terms of its function.

3.3.Identify factors that promote Identify factors that promote complexification.complexification.

PART I: ORIGINSPART I: ORIGINSCentral AssumptionsCentral Assumptions

PART I: ORIGINSPART I: ORIGINSCentral AssumptionsCentral 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?What is Emergent Complexity?What is Emergent Complexity?What is Emergent Complexity?

Emergent phenomena arise from interactions among numerous individual

particles, or “agents.”

Emergent Phenomena – LifeEmergent Phenomena – LifeEmergent Phenomena – LifeEmergent Phenomena – Life

Life’s Origins:Life’s Origins:Four Emergent StepsFour Emergent Steps

Life’s Origins:Life’s Origins:Four Emergent StepsFour Emergent Steps

1. Emergence of biomolecules

2. Emergence of organized molecular systems

3. Emergence of self-replicating molecular systems

4. Emergence of natural selection

Why Is It Difficult to Quantify Complexity?Why Is It Difficult to

Quantify Complexity?

Genomic

Structural X

Behavioral X X

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):

II((EE) = -log) = -log22[[FF((EE)])]

where I(E) is measured in bits.

Functional InformationFunctional InformationFunctional InformationFunctional Information

1. Increase the number of interacting agents.

2. Increase the diversity of interacting agents.

3. Increase selective pressures by environmental cycling

PART III: How to PART III: How to Increase Increase II((EE))PART III: How to PART III: How to Increase Increase II((EE))

Sand Grains

Galaxies

Ant Colonies

The Brain

Implications of Implications of II = -log = -log22[[FF((EE)]:)]:

System Size and DiversitySystem Size and Diversity

Implications of Implications of II = -log = -log22[[FF((EE)]:)]:

System Size and DiversitySystem Size and Diversity

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 Implications of II = -log = -log22[[FF((EE)]:)]:

Cycling and ComplexificationCycling and Complexification

Implications of Implications of II = -log = -log22[[FF((EE)]:)]:

Cycling and ComplexificationCycling and Complexification

Implications of Implications of II = -log = -log22[[FF((EE)]:)]:

Cycling and ComplexificationCycling and Complexification

Implications of Implications of II = -log = -log22[[FF((EE)]:)]:

Cycling and ComplexificationCycling and Complexification

Each cycle has the potential to add information to the system (e.g.,

waves, aptamers, reproduction).

Jack Szostak, Harvard UniversityExperiments in Molecular Evolution

FUNCTIONAL INFORMATIONFUNCTIONAL INFORMATION

Aptamer EvolutionAptamer Evolution

1. Create a random RNA pool

*1

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. Initiate in vitro

selection process

*1

*2

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Wash 15 times to

remove nonbindingstrands

*1

*2

*3

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Remove nonbinding

strands4. Collect bound RNA

strands

*1

*2

*3

*4

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Remove nonbinding

strands4. Collect bound RNA5. Reverse (RNADNA)

transcriptase to copy bound sequences

*1

*2

*3

*4

*5

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Remove nonbinding

strands4. Collect bound RNA5. Reverse transcriptase6. Use PCR to amplify

bound sequences with errors.

*1

*2

*3

*4

*5

*6

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Remove nonbinding

strands4. Collect bound RNA5. Reverse transcriptase6. PCR amplify with

errors7. Transcribe DNA to

new RNA strands

*1

*2

*3

*4

*5

*6

*7

Aptamer EvolutionAptamer Evolution

1. Random RNA pool2. In vitro process3. Remove nonbinding

strands4. Collect bound RNA5. Reverse transcriptase6. PCR amplify with

errors7. Transcribe DNA to

new RNA strands8. Repeat 1 thru 7

*1

*2

*3

*4

*5

*6

*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.: N-alkylation)•Closely resembles tRNA

“ISLANDS OF FITNESS”“ISLANDS OF FITNESS”

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

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.

CONCLUSIONSCONCLUSIONSCONCLUSIONSCONCLUSIONS

With thanks to:

NASA Astrobiology Institute National Science Foundation

Carnegie Institution of Washington