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Protein noise and distribution in a two-stage gene-expression model extended by an mRNA inactivation loop. (English) Zbl 1491.92051

Cinquemani, Eugenio (ed.) et al., Computational methods in systems biology. 19th international conference, CMSB 2021, Bordeaux, France, September 22–24, 2021. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 12881, 215-229 (2021).
Summary: Chemical reaction networks involving molecular species at low copy numbers lead to stochasticity in protein levels in gene expression at the single-cell level. Mathematical modelling of this stochastic phenomenon enables us to elucidate the underlying molecular mechanisms quantitatively. Here we present a two-stage stochastic gene expression model that extends the standard model by an mRNA inactivation loop. The extended model exhibits smaller protein noise than the original two-stage model. Interestingly, the fractional reduction of noise is a non-monotonous function of protein stability, and can be substantial especially if the inactivated mRNA is stable. We complement the noise study by an extensive mathematical analysis of the joint steady-state distribution of active and inactive mRNA and protein species. We determine its generating function and derive a recursive formula for the protein distribution. The results of the analytical formula are cross-validated by kinetic Monte Carlo simulation.
For the entire collection see [Zbl 1486.92002].

MSC:

92C40 Biochemistry, molecular biology
92C42 Systems biology, networks

Software:

GillesPy
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