Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Stochastic Binary Model for Regulation of Gene Expression to Investigate Treatment Effects Targeting RKIP

Version 1 : Received: 21 September 2021 / Approved: 23 September 2021 / Online: 23 September 2021 (11:43:54 CEST)

A peer-reviewed article of this Preprint also exists.

Giovanini, G.; Barros, L.R.C.; Gama, L.R.; Tortelli, T.C., Jr.; Ramos, A.F. A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy. Cancers 2022, 14, 633. Giovanini, G.; Barros, L.R.C.; Gama, L.R.; Tortelli, T.C., Jr.; Ramos, A.F. A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy. Cancers 2022, 14, 633.

Journal reference: Cancers 2022, 14, 633
DOI: 10.3390/cancers14030633

Abstract

In this manuscript we use an exactly solvable stochastic binary model for regulation of gene expression to analyse the dynamics of response to a treatment aiming to modulate the number of transcripts of RKIP gene. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics towards pre-cancerous state: i. to increase the promoter’s ON state duration; ii. to increase the mRNAs’ synthesis rate; iii. to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reducing drug dosage by simultaneously targeting multiple kinetic rates. That enables a reduction of treatment response time and heterogeneity which in principle diminishes the chances of emergence of resistance to treatment. This approach may be useful for inferring kinetic constants related to expression of antimetastatic genes or oncogenes and on the design of multi-drug therapeutic strategies targeting master regulatory genes.

Keywords

RKIP expression regulation; Stochastic binary regulation of gene expression; Treatment targeting RKIP levels increase; Reduction of heterogeneity of treatment response

Subject

LIFE SCIENCES, Biophysics

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