Stochastic modeling for investigation of the regulation of transcription of the RKIP gene

Ramos, Alexandre F.; Gama, Leonardo dos Reis; Morais, Mauro César Cafundó de; Martins, Poliana Cristina de Melo

doi:10.1016/b978-0-12-819612-0.00014-6

Cited by 4 publications

(13 citation statements)

References 60 publications

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“…As a consequence, we expect an increase on the levels of transcripts of RKIP (see scheme in Fig. 2B in [18]).…”

Section: An Effective Model For Investigating Regulation Of Gene Expression Dynamics After Treatmentmentioning

confidence: 93%

“…We suppose the use of a demethylation agent, namely, 5-Azacytidine (5-AzaC), to revert that (see scheme given in Fig. 2A in [18]). Consequently, expression levels of RKIP would be increased as previously tested in triple-negative breast cancer (TNBC) cell line SUM159 and esophageal cell lines TE-1 and TE-13 [37,42].…”

Section: An Effective Model For Investigating Regulation Of Gene Expression Dynamics After Treatmentmentioning

confidence: 99%

“…The master equation ( 10) and (11) with constant coefficients has been already proposed, and it is fully solvable at the stationary [16] and time-dependent regimes [17]. The existence of exact solutions enables one to calculate the time-dependent functions governing the first and the second moment of the number of gene products [18]. Here we write the explicit expressions governing the dynamics of the average number of gene products, n (t) and the standard deviation, σ(t) = n 2 (t) − n 2 (t).…”

Section: An Exactly Solvable Model For Benchmarking Cancer Treatment Aiming To Modulate Gene Expression Levelsmentioning

confidence: 99%

“…Finding building blocks having those features may provide useful insights on how to orchestrate the dynamics of a such a complex system [15]. For that, the exactly solvable stochastic model for transcription of a binary gene [16][17][18][19] is a good candidate to be used as a prototype for simulating enhanced treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

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

Giovanini¹,

Barros²,

Gama³

et al. 2021

Preprint

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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.

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“…As a consequence, we expect an increase on the levels of transcripts of RKIP (see scheme in Fig. 2B in [18]).…”

Section: An Effective Model For Investigating Regulation Of Gene Expression Dynamics After Treatmentmentioning

confidence: 93%

Section: An Effective Model For Investigating Regulation Of Gene Expression Dynamics After Treatmentmentioning

confidence: 99%

Section: An Exactly Solvable Model For Benchmarking Cancer Treatment Aiming To Modulate Gene Expression Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Giovanini¹,

Barros²,

Gama³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finding building blocks with those features may provide useful insights on how to modulate the dynamics of such a complex system [19]. For that, the exactly solvable stochastic model for transcription of a binary gene [20][21][22][23] is a good candidate to be used as a prototype for simulating enhanced treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

Giovanini

Barros

Gama

et al. 2022

Cancers

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In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter’s ON state duration; (2) to increase the mRNAs’ synthesis rate; and (3) 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 reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.

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Modelos binários estocásticos para a expressão gênica: uma análise comparativa das propriedades de ruído e uma investigação de respostas a terapias gênicas

Giovanini

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A conclusão desta dissertação foi devido ao trabalho, aos esforços e ao envolvimento de muitos, direta ou indiretamente. É com muito prazer e sinceridade que agradeço a todos que contribuíram com esta minha realização. Ao Prof. Alexandre, agradeço a orientação, paciência, confiança e tempo investido no meu processo de aprendizado e na minha formação acadêmica. Sua experiência, seus incentivos e recomendações contribuíram significantemente para o meu conhecimento científico e cultural. Agradeço à CAPES e ao PPG-SCX pelos auxílios financeiros recebidos. Agradeço ao IBILCE-UNESP pela minha formação básica. Agradeço à EACH-USP por todos os recursos disponibilizados e aos seus servidores, que mesmo durante a pandemia, mantiveram, com êxito, o funcionamento da universidade e sua comunidade segura. Destaco aqui meus agradecimentos: à Kelly, à Vanessa, ao Jonathan e ao Flávio pelos esclarecimentos e prontidão na realização de trâmites administrativos; aos Profs. M. Lauretto e C. Molina pela disponibilidade e todo o suporte que precisei da coordenação do programa; e aos

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Stochastic modeling for investigation of the regulation of transcription of the RKIP gene

Cited by 4 publications

References 60 publications

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

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

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

Modelos binários estocásticos para a expressão gênica: uma análise comparativa das propriedades de ruído e uma investigação de respostas a terapias gênicas

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