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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 - PowerPoint PPT Presentation
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Why bacteria run Linux while eukaryotes run
Windows?Sergei Maslov
Brookhaven National LaboratoryNew York
2
Physical vs. Biological Laws Physical Laws are 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 Laws is slowed down by us having cookie-cutter explanation in terms of natural selection:
Drawing from Facebook group: Trust me, I'm a "Biologist"'
Genes encoded in bacterial genomes
Packages installed on Linux computers
~
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
different frequencies of installation
Justin Pollard, http://www.designboom.com
IKEA kits have many components
Justin Pollard, http://www.designboom.com
They need to be assembled to work
Different frequencies of use
vs
Common Rare
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 technological systems where selection adjusts undeserved popularity
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
P(f)~ f-1.5 except the top √N “universal” components with f~1
CloudShell
Core
ORFans
TY Pang, S. Maslov, PNAS (2013)
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 = the total number of components that directly or indirectly depend on the selected one
13TY Pang, S. Maslov, PNAS (2013)
Correlation coefficient ~0.4 for both Linux and genesCould be improved by using weighted dependency
degree
Frequency is positively correlated with functional importance
TY Pang, S. Maslov, PNAS (2013)
Warm-up: tree-like metabolic network
Kdep=5
Kdep=15
TCA cycle
TY Pang, S. Maslov, PNAS (2013)
Dependency degree distribution on a critical branching tree
P(K)~K-1.5 for 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
Components added gradually over evolutionary time
New component directly depends on D previously existing components selected randomly
Versions: D is drawn from some distribution
same 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
19
20
Kdep and Kout degree distributions
Kdep decreases layer numberLinux Model with D=2
TY Pang, S. Maslov, PNAS (2013)
Zipf plot for Kdep distributionsMetabolic enzymes
vsModel
Linuxvs
Model
TY Pang, S. Maslov, PNAS (2013)
Frequency distributions
P(f)~ f-1.5 except the top √N “universal” components with f~1
Shell
Core
ORFans
Cloud
TY Pang, S. Maslov, PNAS (2013)
What experiments does P(f) help to interpret?
Pan-genome of E. coli strains
M Touchon et al. PLoS Genetics (2009)
Metagenomes
The Human Microbiome Project Consortium, Nature (2012)
27
Pan-genome scaling
Pan-genome of all bacteria
Slope=-0.4 predictions of the toolbox model (-0.5)
P. LapierreJP Gogarten TIG 2009
(# of genes in pan-genome) ~ (# of sequenced genomes)0.5
(# of new genes added to pan-genome) ~ (# of sequenced genomes)-0.5
Bacterial genome evolution happens in cooperation with
phages
+ =
Comparative genomics of E. coliimplicates phages for BitTorrent
Phage capacity: 20kbOther strains up to
40kb
K-12 to B comparison
1kb: gene length
Phage-Bacteria Infection NetworkData from Flores et al 2011
experiments by Moebus,Nattkemper,1981
WWW from AT&T website circa 1996 visualized by Mark Newman
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
Figure adapted from S. Maslov, TY Pang, K. Sneppen, S. Krishna, PNAS (2009)
# of genes
# of
pat
hway
s (or
thei
r reg
ulat
ors)
101 102 103 104 105100
101
102
103
104
105
# of installed packages
# of
sel
ecte
d pa
ckag
es
100 102 1041.6
1.7
1.8
Linux dataslope 1.7
Nselected packages ~ Ninstalled packages1.7
Software packages for Linux
35
Collaborators: Tin Yau Pang, Stony Brook University
Support: Office of Biological and Environmental Research
Thank you!