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Network Motifs in Prebiotic Metabolic Networks

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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Network Motifs in Prebiotic Metabolic Networks

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  1. Network Motifs in Prebiotic Metabolic Networks Omer Markovitch and Doron Lancet, Department of Molecular Genetics, Weizmann Institute of Science

  2. “Prebiotic Soup” 4,000,000,000 years ago The emergence of the first cell-like entity, the Protocell.

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

  4. 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)

  5. Assemblies / Clusters / Vesicles / Membranes  Composition DNA / RNA / Polymers  Sequence <<Bridging Metabolism and Replicator>> Segre and Lancet, EMBO Reports 1 (2000)

  6. Two scenarios for increasing network complexity RNA world: Increasing node count Lipid World: Increasing node fidelity How the network structure & properties affect evolution ?

  7. 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)

  8. Example of GARD Similarity ‘Carpet’ Following a single lineage. Composome, quasi-stationary state Compositional Similarity

  9. Populations in GARD                Fixed population size.

  10. 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)]

  11. 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

  12. Selection in GARD Positive Negative Hits Based of 1,000 simulations. Markovitch and Lancet, Artificial Life (2012)

  13. 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)

  14. High mutual-catalysis is required for effective evolvability. Too much self-catalysis hampers evolution (dead-end). Metabolic networks tend to be mutualistic. Micro  Macro

  15. 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)

  16. Network motifs in GARD Graded b (weights) Binary b (1, 0) Graded to binary Find motifs Catalytic score ( Feed forward loop {5} )

  17. (omitted from web presentation)

  18. Families of networks Milo et al, Science (2004)

  19. 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.

  20. (omitted from web presentation)

  21. Acknowledgments: Uri Alon. Avi Mayo. Lancet group. Omer Markovitch

  22. 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)

  23. 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).

  24. 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)

  25. GARD model (graded autocatalytic replication domain) Rate enhancement Molecular repertoire Assembly growth backward (leave) forward (join) Fission (split)

  26. 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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