Why bacteria run Linux while eukaryotes run Windows?

1. Why bacteria run Linux while eukaryotes run Windows? Sergei Maslov Brookhaven National Laboratory New York

2. Physical vs. Biological Laws • Physical Lawsare often discovered by finding simple common explanation for very different phenomena • Newton’s Law: • Apples fall to the ground • Planets revolve around the Sun • Discovery of Biological Lawsis slowed down by us having cookie-cutter explanation in terms of natural selection:

3. Drawing from Facebook group: Trust me, I'm a "Biologist"'

4. ~ Genes encoded in bacterial genomes Packages installed on Linux computers

5. Complex systems have many components • Genes (Bacteria) • Software packages (Linux OS) • Components do not work alone: they need to be assembled to work • In individual systems only a subset of components is installed • Genome (Bacteria) – collection of genes • Computer (Linux OS) – collection of software packages • Components have vastly differentfrequencies of installation

6. IKEA kits have many components Justin Pollard, http://www.designboom.com

7. They need to be assembled to work Justin Pollard, http://www.designboom.com

8. Different frequencies of use vs Common Rare

9. What determines the frequency of installation/use of a gene/package? • Popularity: AKA preferential attachment • Frequency ~ self-amplifying popularity • Relevant for social systems: WWW links, facebook friendships, scientific citations • Functional role: • Frequency ~ breadth or importance of the functional role • Relevant for biological and technologicalsystems where selection adjusts undeserved popularity

10. Empirical data on component frequencies • Bacterial genomes (eggnog.embl.de): • 500 sequenced prokaryotic genomes • 44,000 Orthologous Gene families • Linux packages (popcon.ubuntu.com): • 200,000 Linux packages installed on • 2,000,000 individual computers • Binary tables: component is either present or not in a given system

11. Frequency distributions Cloud Shell Core ORFans P(f)~ f-1.5 except the top √N “universal” components with f~1 TY Pang, S. Maslov, PNAS (2013)

12. How to quantify functional importance? • We want to check Frequency ~ Importance • Usefulness=Importance ~ Component is needed for proper functioning of other components • Dependency network • A  B means A depends on B for its function • Formalized for Linux software packages • For metabolic enzymes given by upstream-downstream positions in pathways • Frequency ~ dependency degree, Kdep • Kdep= thetotal number of components that directly or indirectly depend on the selected one

13. TY Pang, S. Maslov, PNAS (2013)

14. Frequency is positively correlated with functional importance Correlation coefficient ~0.4 for both Linux and genes Could be improved by using weighted dependency degree TY Pang, S. Maslov, PNAS (2013)

15. Warm-up: tree-like metabolic network TCA cycle Kdep=15 Kdep=5 TY Pang, S. Maslov, PNAS (2013)

16. Dependency degree distribution on a critical branching tree • P(K)~K-1.5for a critical branching tree • Paradox: Kmax-0.5 ~ 1/N  Kmax=N2>N • Answer: parent tree size imposes a cutoff:there will be √N “core” nodes with Kmax=N • present in almost all systems (ribosomal genes or core metabolic enzymes) • Need a new model: in a tree D=1, while in real systems D~2>1

17. Bottom-down model of dependency network evolution • Components added gradually over evolutionary time • New component directly depends on D previously existing components selected randomly • Versions: • D is drawn from some distributionsame as above • Recent components are preferentially selectedcitations • There is a fixed probability to connect to anypreviously existing componentsfood webs

18. p(t,T) –probability that component added at time T • directly or indirectly depends on one added at time t

20. Kdep and Kout degree distributions

21. Kdep decreases layer number Linux Model with D=2 TY Pang, S. Maslov, PNAS (2013)

22. Zipf plot for Kdep distributions Metabolic enzymes vs Model Linux vs Model TY Pang, S. Maslov, PNAS (2013)

23. Frequency distributions Cloud Core Shell ORFans P(f)~ f-1.5 except the top √N “universal” components with f~1 TY Pang, S. Maslov, PNAS (2013)

24. What experiments does P(f) help to interpret?

25. Pan-genome of E. coli strains M Touchon et al. PLoS Genetics (2009)

26. Metagenomes The Human MicrobiomeProject Consortium, Nature (2012)

27. Pan-genome scaling

28. Pan-genome of all bacteria (# of genes in pan-genome)~ (# of sequenced genomes)0.5 (# of new genes added to pan-genome) ~ (# of sequenced genomes)-0.5  P. LapierreJP GogartenTIG 2009 Slope=-0.4 predictions of the toolbox model (-0.5)

29. Bacterial genome evolution happens in cooperation with phages + =

30. Comparative genomics of E. coliimplicates phages for BitTorrent 1kb: gene length K-12 to B comparison Phage capacity: 20kbOther strains up to 40kb

31. Phage-Bacteria Infection Network Data from Flores et al 2011experiments by Moebus,Nattkemper,1981 WWW from AT&T website circa 1996 visualized by Mark Newman

32. Why eukaryotes run windows? • Dependency network = reuse of components • Bacteria do not keep redundant genes after HGT • Linux developers rely on previous efforts • Pros: smaller genomes, open source, economies of scale • Cons: less specialized, potentially unstable, “dependency hell” • Eukaryotes are like Windows or Mac OS X • Keep redundant components • Proprietary software

33. Figure adapted from S. Maslov, TY Pang, K. Sneppen, S. Krishna, PNAS (2009) # of pathways (or their regulators) # of genes

34. Software packages for Linux • Nselected packages~ Ninstalledpackages1.7

35. Collaborators: Tin Yau Pang, Stony Brook University Support: Office of Biological and Environmental Research

36. Thank you!