Network Motifs in Prebiotic Metabolic Networks
Sponsored Links
This presentation is the property of its rightful owner.
1 / 26

Network Motifs in Prebiotic Metabolic Networks PowerPoint PPT Presentation


  • 64 Views
  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

Network Motifs in Prebiotic Metabolic Networks

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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.


Life is a self-sustaining system capable of undergoing Darwinian evolution.


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)


Assemblies / Clusters / Vesicles / Membranes  Composition

DNA / RNA / Polymers  Sequence

<<Bridging Metabolism and Replicator>>

Segre and Lancet, EMBO Reports 1 (2000)


Two scenarios for increasing network complexity

RNA world: Increasing node count

Lipid World: Increasing node fidelity

How the network structure & properties affect evolution ?


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)


Example of GARD Similarity ‘Carpet’

Following a single lineage.

Composome, quasi-stationary state

Compositional Similarity


Populations in GARD

Fixed population size.


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)]


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


Selection in GARD

Positive

Negative

Hits

Based of 1,000 simulations.

Markovitch and Lancet, Artificial Life (2012)


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)


High mutual-catalysis is required for effective evolvability.

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

Metabolic networks tend to be mutualistic.

Micro  Macro


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)


Network motifs in GARD

Graded b (weights)

Binary b (1, 0)

Graded to binary

Find motifs

Catalytic score

( Feed forward loop {5} )


(omitted from web presentation)


Families of networks

Milo et al, Science (2004)


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.


(omitted from web presentation)


Acknowledgments:

Uri Alon.

Avi Mayo.

Lancet group.

Omer Markovitch


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)


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).


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)


GARD model (graded autocatalytic replication domain)

Rate enhancement

Molecular repertoire

Assembly growth

backward (leave)

forward (join)

Fission (split)


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)


  • Login