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

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

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

4. 4 The Lipid World Scenario for the Origin of Life Spontaneous formation of lipid assemblies may seed life Lipid (Hydrophilic head; Hydrophobic tail) Micelle / Assembly Membrane Segre, Ben-Eli, Deamer and Lancet, Orig. Life Evol. Biosph. 31 (2001) Spontaneous aggregation

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

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

7. 7 GARD model (Graded Autocatalysis Replication Domain) Homeostatic growth Fission / Split  Composition Segre, Ben-Eli and Lancet, Proc. Natl. Acad. Sci. 97 (2000) Solving a set of coupled differential equations, using Gillespie’s algorithm. Symbolic lipids   Environmental Chemistry

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

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

10. 10 j i  ij  ; Catalytic Network of Rate-Enhancments More mutualisticMore selfish  *Self-catalysis is the chemical manifestation of self-replication [Orgel, Nature 358 (1992)]

11. 11 Negative response Positive response Target before selection Target after selection Examples for selection in GARD Slightly biasing the growth rate of assemblies, depending on similarity / dis-similarity to a target composome.

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

13. 13 How the  network effects selection ? Based of 1,000 simulations, each based on a different  network. Probability (log 10 scale)  Self | Mutual  Markovitch and Lancet, Artificial Life (2012)

14. 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. 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. 16 Network motifs in GARD Graded to binary Find motifs ( Feed forward loop {5} ) Graded  (weights) Binary  (1, 0) Catalytic score

17. 17 (omitted from web presentation)

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

19. 19 Principle Component analysis (PCA) Project the 13 th dimensional space of network motifs into another 13 th dimensional space, that maximizes the variance in the original data. For each , a 13-long vector describes its network motifs profile, but this time with linear combination that maximizes the variance.

20. 20 (omitted from web presentation)

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

22. 22 Compotype diversity of 10,000 GARD lineages Each based on a different  network.  Self | Mutual  Probability (log 10 scale) Markovitch and Lancet, Artificial Life (2012)

23. 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. 24 Selection towards a specific target composition Selection of GARD assemblies towards a target compotype. 1)Identify most frequent compotype (= target). 2)Rerun the same simulation while modifying the  ij 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. 25 Assembly growth Fission (split) backward (leave) forward (join) GARD model (graded autocatalytic replication domain) Rate enhancement Molecular repertoire

26. 26 Selection response of 1,000 GARD populations Each based on a different  network. Probability (log 10 scale) Target frequency, before selection Target frequency, after selection Markovitch and Lancet, Artificial Life (2012)