Linking dynamical complexities from activation signals to transcription responses
The transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2019-03-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190286 |
| _version_ | 1819207661484244992 |
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| author | Genghong Lin Feng Jiao Qiwen Sun Moxun Tang Jianshe Yu Zhan Zhou |
| author_facet | Genghong Lin Feng Jiao Qiwen Sun Moxun Tang Jianshe Yu Zhan Zhou |
| author_sort | Genghong Lin |
| collection | DOAJ |
| description | The transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this work, we develop a mathematical model with multiple promoter states to address this question. Each promoter state has its own activation and inactivation rates and is selected randomly with a probability that may change in time. Under the activation of constant signals, our analysis shows that if only the activation rates differ among the promoter states, then the mean transcription level m(t) displays only a monotone or monophasic growth pattern. In a sharp contrast, if the inactivation rates change with the promoter states, then m(t) may display multiphasic growth patterns. Upon the activation of signals that oscillate periodically, m(t) also oscillates later, almost periodically at the same frequency, but the magnitude decreases with frequency and is almost completely attenuated at high frequencies. This gives a surprising indication that multiple promoter states could filter out the signal oscillation and the noise in the random promoter state selection, as observed in the transcription of a gene activated by p53 in breast carcinoma cells. Our approach may help develop a theoretical framework to integrate coherently the genetic circuit with the promoter states to elucidate the linkage from the activation signal to the temporal profile of transcription outputs. |
| first_indexed | 2024-12-23T05:27:02Z |
| format | Article |
| id | doaj.art-57a0c84edef9428996633d971eaf94df |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-23T05:27:02Z |
| publishDate | 2019-03-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-57a0c84edef9428996633d971eaf94df2022-12-21T17:58:35ZengThe Royal SocietyRoyal Society Open Science2054-57032019-03-016310.1098/rsos.190286190286Linking dynamical complexities from activation signals to transcription responsesGenghong LinFeng JiaoQiwen SunMoxun TangJianshe YuZhan ZhouThe transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this work, we develop a mathematical model with multiple promoter states to address this question. Each promoter state has its own activation and inactivation rates and is selected randomly with a probability that may change in time. Under the activation of constant signals, our analysis shows that if only the activation rates differ among the promoter states, then the mean transcription level m(t) displays only a monotone or monophasic growth pattern. In a sharp contrast, if the inactivation rates change with the promoter states, then m(t) may display multiphasic growth patterns. Upon the activation of signals that oscillate periodically, m(t) also oscillates later, almost periodically at the same frequency, but the magnitude decreases with frequency and is almost completely attenuated at high frequencies. This gives a surprising indication that multiple promoter states could filter out the signal oscillation and the noise in the random promoter state selection, as observed in the transcription of a gene activated by p53 in breast carcinoma cells. Our approach may help develop a theoretical framework to integrate coherently the genetic circuit with the promoter states to elucidate the linkage from the activation signal to the temporal profile of transcription outputs.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190286stochastic gene transcriptionpromoter statesmean transcription leveloscillation and noise filtrationdynamical complexity |
| spellingShingle | Genghong Lin Feng Jiao Qiwen Sun Moxun Tang Jianshe Yu Zhan Zhou Linking dynamical complexities from activation signals to transcription responses Royal Society Open Science stochastic gene transcription promoter states mean transcription level oscillation and noise filtration dynamical complexity |
| title | Linking dynamical complexities from activation signals to transcription responses |
| title_full | Linking dynamical complexities from activation signals to transcription responses |
| title_fullStr | Linking dynamical complexities from activation signals to transcription responses |
| title_full_unstemmed | Linking dynamical complexities from activation signals to transcription responses |
| title_short | Linking dynamical complexities from activation signals to transcription responses |
| title_sort | linking dynamical complexities from activation signals to transcription responses |
| topic | stochastic gene transcription promoter states mean transcription level oscillation and noise filtration dynamical complexity |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190286 |
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