Abstract
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.
CÇ is supported by the Comenius University grant for doctoral students Nos. UK/106/2020 and UK/100/2021. PB is supported by the Slovak Research and Development Agency under the contract No. APVV-18-0308 and by the VEGA grant 1/0339/21, and the EraCoSysMed project 4D-Healing. AS acknowledges support by ARO W911NF-19-1-0243 and NIH grants R01GM124446 and R01GM126557.
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Çelik, C., Bokes, P., Singh, A. (2021). Protein Noise and Distribution in a Two-Stage Gene-Expression Model Extended by an mRNA Inactivation Loop. In: Cinquemani, E., Paulevé, L. (eds) Computational Methods in Systems Biology. CMSB 2021. Lecture Notes in Computer Science(), vol 12881. Springer, Cham. https://doi.org/10.1007/978-3-030-85633-5_13
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