title: Strong negative self regulation of prokaryotic transcription factors increases the intrinsic noise of protein expression.
abstract: BACKGROUND
  Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.
  
  
  RESULTS
  We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels.
  
  
  CONCLUSIONS
  Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors.
authors.affiliation: Centre for Systems Biology, School of Biosciences, University of Birmingham, Edgbaston B15 2TT, UK. d.j.stekel@bham.ac.uk
authors.forename: Dov J
authors.initials: DJ
authors.lastname: Stekel
authors.forename: Dafyd J
authors.initials: DJ
authors.lastname: Jenkins
mesh_terms: D000704:Analysis of Variance; D005075:Biological Evolution; D003198:Computer Simulation; D015536:Down-Regulation; D019848:Gene Expression Regulation, Archaeal / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics; D006706:Homeostasis / Q000235:genetics; D008957:Models, Genetic; D011387:Prokaryotic Cells / Q000378:metabolism*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism*; D014158:Transcription, Genetic
chemical_list: D012333:RNA, Messenger; D014157:Transcription Factors