home depot model of evolution of prokaryotic metabolic networks and their regulation n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation PowerPoint Presentation
Download Presentation
"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation

Loading in 2 Seconds...

play fullscreen
1 / 57

"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation. Sergei Maslov Brookhaven National Laboratory. In collaboration with Kim Sneppen and Sandeep Krishna, Center for Models of Life, Copenhagen U and Tin Yau Pang, Stony Brook U .

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation' - margot


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
home depot model of evolution of prokaryotic metabolic networks and their regulation

"Home Depot" Model of Evolution of Prokaryotic Metabolic Networks and Their Regulation

Sergei Maslov

Brookhaven National Laboratory

In collaboration with Kim Sneppen and Sandeep Krishna, Center for Models of Life, Copenhagen U

andTin Yau Pang, Stony Brook U

slide2

Stover et al., Nature (2000)

van Nimwegen, TIG (2003)

the rise of bureaucracy
The rise of bureaucracy!
  • Fraction of bureaucrats grows with organization size
  • Trend (if unchecked) could lead to a “bureaucratic collapse”: 100% bureaucrats and no workers
  • As human bureaucrats, transcription factors are replaceable and disposable
    • many anecdotal stories of one regulator replacing another in closely related organisms
    • Not very essential (at least in yeast) .
  • One is tempted to view regulators nearly as “parasites” or superficial add-ons that marginally improve the efficiency of an organism
slide5

Encephalization QuotientEQ~M(brain)1//M(body)

From Carl Sagan's book: “Dragons of Eden: Speculations on the Evolution

of Human Intelligence”

bacterial iq
Bacterial IQ

Bacterial IQ~(Nsignal trasnsducers)1/2/Ngenes

Table from M.Y. Galperin, BMC Microbiology (2005)

quadratic scaling applies to all types of regulation and signaling
Quadratic scaling applies to all types of regulation and signaling

Table from Molina, van Nimwegen, Biology Direct 2008

let s play with this scaling law
Let’s play with this scaling law
  • NR=NG2/80,000 --> NR=NG 2NG/80,000
  • NG /NR=40,000/NG
    • ~40 new genes per regulator for NG=1000
    • ~4 new genes (1 regulator + 3 non-regulatory genes) for the largest bacterial genomes with NG~10,000
  • Important observation: NG /NR decreases with genome size
slide10

Now to our model

Disclaimer: authors of this study (unfortunately) receivedno financial support from Home Depot, Inc. or Obi, GMBH

home depot argument
“Home Depot” argument
  • Inspired by personal experience as a new homeowner buying tools
  • Tools are bought to accomplish functional tasks e.g. fix a leaking faucet
  • Redundant toolsare returned to “Home Depot”
  • As your toolbox grows you need to get fewer and fewer new tools to accomplish a new task
  • Tools are e.g. metabolic pathways acquired by Horizontal Gene Transfer
  • Regulators control these pathways (we assume one regulator per task/pathway)
  • Redundant genes are promptly deleted (in prokaryotes)
  • Genomes shrink by deleting entire pathways that are no longer required
  • All non-regulatory “workhorse” genes of an organism - its toolbox
  • As it gets larger you need fewer new workhorse genes per new regulated function – FASTER THAN LINEAR SCALING
random overlap between functions no quadratic scaling
Random overlap between functions  no quadratic scaling!
  • Nuniv – the total number of tools in “Home Depot”
  • NG – the number of tools in my toolbox
  • Lpathway – the number of tools needed for each new functional task
  • If overlap is random then Lpathway NG / Nuniv are redundant (already in the toolbox)
  • dNG/dNR= Lpathway- Lpathway NG / Nuniv
  • Superlinear only due to logarithmic corrections: NG= Lpathway NR / Nuniv log NRmax/(NRmax-NR)
  • Networks are needed for non-random overlap between functional pathways
slide14

Central metabolism  anabolic pathways  biomass

Pathways could be also removed

nutrient

Horizontal gene transfer:entire pathways could be added in one step

nutrient

nutrient

slide15

New pathways are added from the universal network formed by the union of all reactions in all organisms (bacterial answer to “Home depot”)

  • The only parameter - the size of the universal network Nuniv
  • The current size of the toolbox (# of genes ~ # of enzymes ~ # of metabolites): NG
  • Probability to join the existing pathway: pjoin= NG /Nuniv
  • Lpathway=1/pjoin=Nuniv/NG
  • If one regulator per pathway: NG/NR=Lpathway=Nuniv/NG
  • Quadratic law:NR=NG2 /2Nuniv

=

+

we tried several versions of the toolbox model
We tried several versions of the toolbox model
  • On a random network: analytically solved to give NR~Nmet2
    • On a union of all KEGG reactions: numerically solved to give NR~Nmet1.8
    • ~1800 reactions and metabolites upstream of the central metabolism
    • Randomly select nutrients
    • Follow linear pathways until they overlap with existing network
slide17

Green – all fully sequenced prokaryotesRed – toolbox model on KEGG universal network with Nuniv=1800

From SM, S. Krishna, T.Y. Pang, K. Sneppen, PNAS (2009)

slide18

Length distribution of metabolic pathways/branches

-1=2

Green – linear branches in E. coli metabolic network Red – toolbox model on KEGG

SM, S. Krishna, T.Y. Pang, K. Sneppen, PNAS (2009)

model with shortest branched instead of meandering linear pathways
Model with shortest & branched instead of meandering & linear pathways

Slope=1.7

SM, T.Y. Pang, in preparation (2010)

what does it mean for regulatory networks
What does it mean for regulatory networks?
  • NR<Kout>=NG<Kin>=number of regulatory interactions
  • NR/NG= <Kin>/<Kout> increases with NG
    • Either <Kout> decreases with NG: pathways become shorter as in our model
    • Or <Kin>grows with NG:regulation gets more coordinated
    • Most likely both trends at once

E. van Nimwegen, TIG (2003)

regulating pathways basic version

nutrient

nutrient

Regulating pathways: basic version

<Kout>: 

<Kin>=1=const

TF1

TF2

regulating pathways long regulons

nutrient

nutrient

Regulating pathways: long regulons

<Kout>=const

<Kin>: 

TF1

TF2

conclusions and future plans
Conclusions and future plans
  • Toolbox “Home Depot” model explains:
    • Quadratic scaling of the number of regulators
    • Broad distribution (hubs and stubs) of regulon sizes:most functions need few tools, some need many
  • Gene duplication models offer an alternative way to explain hubs in biological networks but the ultimate explanation has to be functional
  • Our model relies on Horizontal Gene Transfer instead of gene duplication
  • To do list:
    • Coordination of regulation of different pathways: which of our proposed scenarios (if any) is realized?
    • What Nature is trying to minimize when adding branched pathways? The number of added reactions? The number of byproducts? Cross-talk with existing pathways?
    • Extensions to organizations, technology innovations, etc?
slide27

Target product

By-product

“Surface”

By-product

NM

slide31

Toolbox model

E. coli metabolic network (spanning tree)

deleting pathways

nutrient

nutrient

nutrient

Deleting pathways

TF1

TF1

TF2

slide34

Distribution of regulon sizes

Green – regulons in E. coli Red – toolbox model on full KEGG

bacterial iq1
Bacterial IQ

IQ~(Nsignal trasnsducers)1/2/Ngenes

Table from M.Y. Galperin, BMC Microbiology (2005)

slide37

KEGG pathways vs reactions

In ~500 fully sequenced prokaryotes

# of pathways ~ NR

# of reactions ~ NG

SM, S. Krishna, K. Sneppen (2008)

slide40

Adaptive evolution of bacterial metabolic networks by horizontal gene transfer

Csaba Pal, Balazs Papp & Martin Lercher, Nat. Gnet. (2005)

slide41

Adaptive evolution of bacterial metabolic networks by horizontal gene transfer

Csaba Pal, Balazs Papp & Martin Lercher, Nat. Gnet. (2005)

slide42

nutrient

nutrient

nutrient

TF1

TF2

complexity is manifested in k in distribution
Complexity is manifested in Kin distribution

E. coli vs. S. cerevisiae vs. H. sapiens

slide48

Coordinated activity of pathways

Basic version

SM, S. Krishna, K. Sneppen (2008)

slide50

Jerison 1983 The evolution of the mammalian brain as an information-processing system. pp. 113-146 IN Eisenberg, J. F. & Kleiman, D. G. (Ed.),

Advances in the Study of Mammalian Behavior

(Spec. Publ. Amer. Soc. Mamm. 7).

Pittsburgh: American Society of Mammalogists. Figure redrawn from Jerison 1973

Jerison 1983

trivia facts
Trivia facts
  • Zebrafish – the largest # of TFs (~2700) or 10% of ~27,000 genes. (humans ~1900 TFs or 8% of 24,000 genes)
  • In bacteria it is Burkholderia sp. 383 : ~800 TFs out of 8000 genes (also 10% of the total)
  • Linear fit to log(NR) with log(Ngenes) explains 87% of the variance (cc~0.93)
  • Linear fit to NR/Ngenes with Ngenes explains 50%-60% of the variance (cc~0.7-0.75).
slide53

http://www.g-language.org/g3/

Gut/sewer bacterium: E. coli K12: 4467 genes 271 TFs 6%

slide54

http://www.g-language.org/g3/

Aphid parasite: Buchnera aphidicola APS: 618 genes 6 TFs 1%

slide55

http://www.g-language.org/g3/

Soil bacterium: Rhodococcus sp. RHA1: 9221 genes 641 TFs 7%

slide56

http://www.g-language.org/g3/

Gut/free bacterium: E. coli K12: 4467 genes 271 TFs 6%