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Why bacteria run Linux while eukaryotes run Windows?. Sergei Maslov Brookhaven National Laboratory New York. Physical vs. Biological Laws. Physical Laws are often discovered by finding simple common explanation for very different phenomena Newton’s Law : A pples fall to the ground

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why bacteria run linux while eukaryotes run windows

Why bacteria run Linux while eukaryotes run Windows?

Sergei Maslov

Brookhaven National Laboratory

New York

physical vs biological laws
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:
slide4

~

Genes encoded in bacterial genomes

Packages installed on Linux computers

slide5

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
ikea kits have many components
IKEA kits have many components

Justin Pollard, http://www.designboom.com

they need to be assembled to work
They need to be assembled to work

Justin Pollard, http://www.designboom.com

what determines the frequency of installation use of a gene package
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
empirical data on component frequencies
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
frequency distributions
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)

how to quantify functional importance
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
frequency is positively correlated with functional importance
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)

warm up tree like metabolic network
Warm-up: tree-like metabolic network

TCA cycle

Kdep=15

Kdep=5

TY Pang, S. Maslov, PNAS (2013)

dependency degree distribution on a critical branching tree
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
bottom down model of dependency network evolution
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
slide18

p(t,T) –probability that component added at time T

  • directly or indirectly depends on one added at time t
k dep decreases layer number
Kdep decreases layer number

Linux

Model with D=2

TY Pang, S. Maslov, PNAS (2013)

zipf plot for k dep distributions
Zipf plot for Kdep distributions

Metabolic enzymes

vs

Model

Linux

vs

Model

TY Pang, S. Maslov, PNAS (2013)

frequency distributions1
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)

pan genome of e coli strains
Pan-genome of E. coli strains

M Touchon et al. PLoS Genetics (2009)

metagenomes
Metagenomes

The Human MicrobiomeProject Consortium, Nature (2012)

p an genome of all bacteria
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)

comparative genomics of e coli implicates phages for bittorrent
Comparative genomics of E. coliimplicates phages for BitTorrent

1kb: gene length

K-12 to B comparison

Phage capacity: 20kbOther strains up to 40kb

slide31

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

why eukaryotes run windows
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
slide33

Figure adapted from S. Maslov, TY Pang, K. Sneppen, S. Krishna, PNAS (2009)

# of pathways (or their regulators)

# of genes

software packages for linux
Software packages for Linux
  • Nselected packages~ Ninstalledpackages1.7
slide35

Collaborators: Tin Yau Pang, Stony Brook University

Support:

Office of Biological and Environmental Research