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Stochastic gene expression ________________________________________________


Joyoti Basu, Indrani Bose, Manikuntala Kundu

Stochastic Gene Expression: Effect of positive feedback and gene expression noise in mycobacterial persistence (see also Phenotypic Heterogeneity):
 

Indrani Bose and Rajesh Karmakar

Graded versus Binary Gene Expression (GE):

Binary GE
 Binary gene expression seen in distribution of protein levels.

  • Have proposed a purely stochastic origin of binary gene expression consistent with experimental observations.
  • Stochatic binary GE: no bistability in the deterministic case (See reviews by Kaern et al., Nature Reviews Genetics 6, 451 (2005); Raj et al., Cell  135, 216 (2008) ).
  • Derived conditions for graded and binary GE in terms of GE parameters
Further details: Graded and binary responses in stochastic gene expression by Karmakar R. and Bose I., Physical Biology 1, 197 (2004)
 

Stochastic Origins of Haploinsufficiency (HI):

  • HI:  Genetic disorder brought about by loss in gene copy number, occurs in diploid organisms. Examples of HI: certain types of cancer and diabetes
  • Reduced number of genes: larger fluctuations in protein levels
  • Finite probability that  level falls below  threshold for  onset of protein activity
  • Loss of important protein function: may give rise to diseases
  • Have calculated analytically the probability that a protein level falls below a threshold level due to fluctuation
Further details: Mathematical models of haploinsufficiency by Bose I. and Karmakar R. in Biology of Genetic Dominance ed. by Veitia R. (Landes Bioscience USA 2006),  page 76


Positive Feedback, Stochasticity and Genetic Competence:

Stochastic simulation of competence development in a small subpopulation

Stochastic simulation of competence development in a small subpopulation

  • Have developed a mathematical model to probe how a fraction of B. subtilis population develops competence under stress.
  • Have proposed three mechanisms for the development of heterogeneity (See review by Veening et al., Ann. Rev. Microbiol. 62, 193 (2008); see also To et al., Science 327, 1142 (2010) for an experimental verification of one of the mechanisms)
Further details: Positive feedback, stochasticity and genetic competence by Karmakar R. and  Bose I.,  Physical Biology 4, 29 (2007)


Indrani Bose and Bhaswar Ghosh

     Noise Characteristics of  Feed Forward Loops:

3

Four types of coherent feed forward loops; X, Y, Z denote genes

  • Have shown that the Type-1 coherent feedforward loop (FFL) acts as the best  noise filter amongst the four coherent feed forward loops.
  • This particular type of FFL is the most abundant FFL in the gene transcription regulatory networks of E.coli and S.cerevisiae
  • Speculation: noise is one parameter on which natural evolution work (See review by Uri Alon, Nature Review Genetics 8, 450 (2007))
Further details: Noise characteristics of feed forward loops by Ghosh B., Karmakar R. and Bose I., Physical Biology 2, 36 (2005)



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