NetSciReg'13 - Network Models in Cellular Regulation
June 4, 2013 - Copenhagen

 NetSciReg'13 Flyer 
 Important Dates 
 Call for Contributed Talks 
 NetSci 2013 

Time: 3:15 - 4:05 PM

Type: Invited presentation

Affiliation: Brookhaven National Laboratory, USA


It has been reported [1] that in prokaryotic genomes the number of transcriptional regulators is proportional to the square of the total number of genes. As a consequence of this trend their fraction among all genes (the so-called "regulatory overhead" of a genome) is less than 0.5% in small (< 500 genes) genomes, while in large genomes (~10,000 genes) it can be as high as 10%. The situation is reminiscent of the humorous Parkinson's Law describing the rate at which government bureaucracies disproportionately expand over time. We recently proposed [2] a general explanation of the quadratic scaling of regulators in bacterial genomes and illustrated it using a simple model in which metabolic and regulatory networks co-evolve together. In our model organisms acquire new metabolic functions by the virtue of horizontal gene transfer of entire co-regulated metabolic pathways from a shared gene pool (the "universal metabolic network"). Adapting to a new environmental condition (e.g. learning to use a new nutrient source) involves acquiring new enzymes as well as reusing some of the enzymes that are already encoded in the genome. As organism's genome grows larger it is more likely have some of the genes necessary to master a new functional task and thus needs to acquire fewer new genes from the environment. From this argument it follows that the number of functional tasks equal to the number of their transcriptional regulators should always scale faster than linearly with the total number of genes in the genome. The empirically observed quadratic scaling between these two numbers was mathematically derived for a broad range of universal network topologies [3] as well as reconciled [4] with the scaling law describing families of homologous proteins. Evolutionary conserved pathways in our model have a long-tailed power-law distribution of sizes that agrees well with real-life data. This offers a conceptual explanation for the empirically observed broad distribution of regulon sizes defined by out-degrees of transcription factors in regulatory networks.

  • [1] E van Nimwegen, "Scaling laws in the functional content of genomes", Trends Genet 19:479-84 2003.
  • [2] S Maslov, S Krishna, T Y Pang, K Sneppen, "Toolbox model of evolution of prokaryotic metabolic networks and their regulation", PNAS 106, 9743-9748 2009.
  • [3] TY Pang, S Maslov, "Toolbox model of evolution of metabolic pathways on networks of arbitrary topology" PLoS Comp. Bio 7, e1001137 2011.
  • [4] J Grilli, B Bassetti, S Maslov, MC Lagomarsino, "Joint scaling laws in functional and evolutionary categories in prokaryotic genomes", Nucleic Acids Research 40 530-540 2012.