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Network Motifs in Prebiotic Metabolic Networks. Omer Markovitch and Doron Lancet, Department of Molecular Genetics, Weizmann Institute of Science. “Prebiotic Soup” 4,000,000,000 years ago. The emergence of the first cell-like entity, the Protocell.

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slide1

Network Motifs in Prebiotic Metabolic Networks

Omer Markovitch and Doron Lancet,

Department of Molecular Genetics,

Weizmann Institute of Science

slide2

“Prebiotic Soup”

4,000,000,000 years ago

The emergence of the first cell-like entity, the Protocell.

slide4

The Lipid World Scenario for the Origin of Life

Spontaneous formation of lipid assemblies may seed life

Spontaneous aggregation

Micelle / Assembly

Lipid

(Hydrophilic head; Hydrophobic tail)

Membrane

Segre, Ben-Eli, Deamer and Lancet, Orig. Life Evol. Biosph. 31 (2001)

slide5

Assemblies / Clusters / Vesicles / Membranes  Composition

DNA / RNA / Polymers  Sequence

<<Bridging Metabolism and Replicator>>

Segre and Lancet, EMBO Reports 1 (2000)

slide6

Two scenarios for increasing network complexity

RNA world: Increasing node count

Lipid World: Increasing node fidelity

How the network structure & properties affect evolution ?

slide7

GARD model (Graded Autocatalysis Replication Domain)

Homeostatic growth

b

Composition

Symbolic lipids

Fission / Split

Solving a set of coupled differential equations, using Gillespie’s algorithm.

b Environmental Chemistry

Segre, Ben-Eli and Lancet, Proc. Natl. Acad. Sci. 97 (2000)

slide8

Example of GARD Similarity ‘Carpet’

Following a single lineage.

Composome, quasi-stationary state

Compositional Similarity

slide9

Populations in GARD

Fixed population size.

slide10

b ; Catalytic Network of Rate-Enhancments

bij

j

i

bij

b

More mutualistic

More selfish

*Self-catalysis is the chemical manifestation of self-replication [Orgel, Nature 358 (1992)]

slide11

Examples for selection in GARD

Slightly biasing the growth rate of assemblies, depending on similarity / dis-similarity to a target composome.

Target before selection

Target after selection

Positive response

Negative response

slide12

Selection in GARD

Positive

Negative

Hits

Based of 1,000 simulations.

Markovitch and Lancet, Artificial Life (2012)

slide13

How the b network effects selection ?

Probability (log10 scale)

Based of 1,000 simulations, each based on a different b network.

Self | Mutual

Markovitch and Lancet, Artificial Life (2012)

slide14

High mutual-catalysis is required for effective evolvability.

Too much self-catalysis hampers evolution (dead-end).

Metabolic networks tend to be mutualistic.

Micro  Macro

slide15

So we need more mutual-catalysis 

But of what type / shape?

Network motifs – design patterns of nature. (sub-graphs that appear more then random)

Uri Alon, Nature Review Genetics (2007)

slide16

Network motifs in GARD

Graded b (weights)

Binary b (1, 0)

Graded to binary

Find motifs

Catalytic score

( Feed forward loop {5} )

slide18

Families of networks

Milo et al, Science (2004)

slide19

Principle Component analysis (PCA)

Project the 13th dimensional space of network motifs into another 13th dimensional space, that maximizes the variance in the original data.

For each b, a 13-long vector describes its network motifs profile, but this time with linear combination that maximizes the variance.

slide21

Acknowledgments:

Uri Alon.

Avi Mayo.

Lancet group.

Omer Markovitch

slide22

Compotype diversity of 10,000 GARD lineages

Each based on a different b network.

Probability (log10 scale)

Self | Mutual

Markovitch and Lancet, Artificial Life (2012)

slide23

Real GARD (Rafi Zidovezki from U. California Riverside)

Real lipids: phosphate-idyl-(serine / amine / choline), sphingo-myelin and cholesterol.

Actual physical properties (charge, length, unsaturation).

R = -0.85

Armstrong, Markovitch, Zidovetzki and Lancet, Phys. Biol. 8 (2011).

slide24

Selection towards a specific target composition

  • Selection of GARD assemblies towards a target compotype.
  • Identify most frequent compotype (= target).
  • Rerun the same simulation while modifying the bij values at each generation, biasing the growth rate towards the target.

H: compositional similarity between current and target.

Markovitch and Lancet, Artificial Life (2012)

slide25

GARD model (graded autocatalytic replication domain)

Rate enhancement

Molecular repertoire

Assembly growth

backward (leave)

forward (join)

Fission (split)

slide26

Selection response of 1,000 GARD populations

Probability (log10 scale)

Target frequency, after selection

Target frequency, before selection

Each based on a different b network.

Markovitch and Lancet, Artificial Life (2012)

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