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Parkinson’s Law in bacterial regulation

Parkinson’s Law in bacterial regulation. Sergei Maslov Brookhaven National Laboratory. Regulation inside bacteria. Genomes of bacteria contain between several 100s to 10,000s genes

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Parkinson’s Law in bacterial regulation

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  1. Parkinson’s Law in bacterial regulation

    Sergei Maslov Brookhaven National Laboratory
  2. Regulation inside bacteria Genomes of bacteria contain between several 100s to 10,000s genes Only a small subset of proteins encoded by these genes is needed under any given environmental condition Protein production from genes is turned on and off by special regulatory genes – transcription factors often in response to environmental signals
  3. LacZ LacY LacA Lactose LacI How E. coliutilizes lactose
  4. How many regulatorsdoes a bacterium need? Transcription factors “Workhorse” genes
  5. Stover et al., Nature (2000), van Nimwegen, TIG (2003)figure from Maslov et al. PNAS (2007) NR=NG2/80,000  NR/NG= NG/80,000 +
  6. The total of those employed inside a bureaucracy grew by 5-7% per year "irrespective of any variation in the amount of work (if any) to be done." Parkinson explains the growth of bureaucracy by two forces: "An official wants to multiply subordinates, not rivals" "Officials make work for each other." Is this what happens in bacterial genomes? Probably not!
  7. Economies of scale in bacterial evolution NR=NG2/80,000  NG/NR=80,000/NG Economies of scale: as genome gets largerit gets easier to add new pathways as they get shorter
  8. Pathways could be also removed nutrient Horizontal gene transfer:entire pathways could be added in one step nutrient Redundant enzymes are removed Central metabolic core  anabolic pathways  biomass production
  9. “Home Depot” or toolbox model Disclaimer: authors of this study (unfortunately) receivedno financial support from Home Depot, Inc. Homebase, LTD or Obi, GMBH
  10. Bottom-downmodeling metabolic networks Milk Food Waste Spherical cow approximation
  11. New pathways come from the “universal metabolic network”of size Nuniv: the union of all reactions in all organisms (bacterial answer to “Home depot”) Metabolic network in a given bacterium(# of enzymes ~ # of metabolites): NG Probability of a new pathway to merge with existing pathways: pmerge= NG /Nuniv Length before merger: Ladded pathway=1/pmerge=Nuniv/NG Assume one regulator per function/pathway: ΔNG/ΔNR=Ladded pathway+1 ~ Nuniv/NG Quadratic law:NR=NG2 /2Nuniv
  12. Toolbox model E. coli metabolic network (spanning tree)
  13. Inspired by “scope-expansion” algorithm by Reinhart Heinrich and collaborators TY Pang, S. Maslov, PNAS 2011
  14. Model with multi-substrate & multi-products reactions from KEGG andminimal pathways TY Pang, S. Maslov, PNAS 2011
  15. What about non-metabolic genes? P(U)~U-γ=U-1.5 Does not work for P(U)=const
  16. Software packages for Linux Nselected packages~ Ninstalled packages1.7
  17. What it all means for regulatory networks? Trends in complexity of regulation vs. genome size NR<Kout>=NG<Kin>=number of edges in a regulatory network NR/NG= <Kin>/<Kout> increases with NG Either <Kout> decreases with NG:functions become more specialized Or <Kin>grows with NG:regulation gets more coordinated & interconnected Most likely both trends at once E. van Nimwegen, TIG (2003)
  18. nutrient Regulatory templates:one worker – one boss <Kout>:  <Kin>=1=const TF1 nutrient TF2
  19. nutrient Regulatory templates:long top-to-bottom regulation <Kout>=const <Kin>: TF1 nutrient <Kout>: <Kin> : TF2
  20. nutrient Hierarchy & middle management:too slow! TF1 nutrient TF2 TF3
  21. nutrient One hub to rule them all (CRP) TF3 TF1 nutrient TF2
  22. Predictions of the toolbox model Powerlaw distribution of pathways sizes: (# of pathways of size S) ~ (S, # of genes in a pathway)-3 Same as powerlaw distribution of regulon sizes = out-degrees of TFs in the regulatory network?
  23. Distribution of regulon sizes Green – regulons in E. coli from RegulonDBRed – KEGG toolbox model
  24. nutrient Regulon size distribution TF1 nutrient TF2
  25.  PavelNovichkov and collaborators, LBL
  26.  PavelNovichkov and collaborators, LBL
  27. Take home messages Contrary to human organizations Parkinson’s law does not apply to bacterial genomes: Thanks, natural selection! Economies of scale make it easier to add pathways to large genomes Open questions: What sets the upper bound of 10,000 genes in bacterial genomes? Model of overlap between regulons and pathways? How to describe non-metabolic TFs and genes? Apply toolbox to other systems: see Linux on Thursday
  28. CollaboratorsandFunding Toolbox model: Tin Yau Pang (Stony Brook) Kim Sneppen(CMOL, NBI Copenhagen) Sandeep Krishna (NCBS, India) Marco C.Lagomarsino (U. of Pierre and Marie Curie, Paris) Jacopo Grilli (U. of Milano) Bruno Bassetti(U. of Milano) Kbase: Adam Arkin (Berkeley) Rick Stevens (Argonne) Bob Cottingham (Oak Ridge) PavelNovichkov (LBL) Mark Gerstein (Yale) Doreen Ware (Cold Spring Harbor) David Weston (Oak Ridge) 60+ other collaborators US Department Of Energy, Office of Biological and Environmental Research Systems Biology Knowledgebase(KBase)Visit us @ kbase.us
  29. Thank you!
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