Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention
Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does mo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-06-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022392?viewType=HTML |
| _version_ | 1818216764918464512 |
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| author | Qiqi Deng Aimin Chen Huahai Qiu Tianshou Zhou |
| author_facet | Qiqi Deng Aimin Chen Huahai Qiu Tianshou Zhou |
| author_sort | Qiqi Deng |
| collection | DOAJ |
| description | Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes. |
| first_indexed | 2024-12-12T06:57:10Z |
| format | Article |
| id | doaj.art-a1f02bf17ff4474687f0d1628040a669 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-12T06:57:10Z |
| publishDate | 2022-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-a1f02bf17ff4474687f0d1628040a6692022-12-22T00:33:56ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-06-011988426845110.3934/mbe.2022392Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retentionQiqi Deng 0Aimin Chen1Huahai Qiu2Tianshou Zhou 31. Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China2. School of Mathematics and Statistics, Henan University, Kaifeng 475004, China3. School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China1. Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-sen University, Guangzhou 510275, ChinaTranscription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.https://www.aimspress.com/article/doi/10.3934/mbe.2022392?viewType=HTMLtranscriptionrna nuclear retentionmolecular memorynon-markov modeltranscription noise |
| spellingShingle | Qiqi Deng Aimin Chen Huahai Qiu Tianshou Zhou Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention Mathematical Biosciences and Engineering transcription rna nuclear retention molecular memory non-markov model transcription noise |
| title | Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention |
| title_full | Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention |
| title_fullStr | Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention |
| title_full_unstemmed | Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention |
| title_short | Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention |
| title_sort | analysis of a non markov transcription model with nuclear rna export and rna nuclear retention |
| topic | transcription rna nuclear retention molecular memory non-markov model transcription noise |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022392?viewType=HTML |
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