Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.
Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectiv...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013014 |
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| author | Zhanhao Zhang Iryna Zabaikina Cesar Nieto Zahra Vahdat Pavol Bokes Abhyudai Singh |
| author_facet | Zhanhao Zhang Iryna Zabaikina Cesar Nieto Zahra Vahdat Pavol Bokes Abhyudai Singh |
| author_sort | Zhanhao Zhang |
| collection | DOAJ |
| description | Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives can lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth. This protein inhibits growth at high concentrations, establishing a positive feedback loop. Using a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth. |
| format | Article |
| id | doaj-art-a31eb9ce7caa4aeb876b273951c450a7 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-a31eb9ce7caa4aeb876b273951c450a72025-08-20T02:09:51ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-06-01216e101301410.1371/journal.pcbi.1013014Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.Zhanhao ZhangIryna ZabaikinaCesar NietoZahra VahdatPavol BokesAbhyudai SinghRandom fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives can lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth. This protein inhibits growth at high concentrations, establishing a positive feedback loop. Using a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.https://doi.org/10.1371/journal.pcbi.1013014 |
| spellingShingle | Zhanhao Zhang Iryna Zabaikina Cesar Nieto Zahra Vahdat Pavol Bokes Abhyudai Singh Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. PLoS Computational Biology |
| title | Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. |
| title_full | Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. |
| title_fullStr | Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. |
| title_full_unstemmed | Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. |
| title_short | Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives. |
| title_sort | stochastic gene expression in proliferating cells differing noise intensity in single cell and population perspectives |
| url | https://doi.org/10.1371/journal.pcbi.1013014 |
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