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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2503.00776 |
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| _version_ | 1866912255499567104 |
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| author | Song, Yun Min Lee, Kangmin Kim, Jae Kyoung |
| author_facet | Song, Yun Min Lee, Kangmin Kim, Jae Kyoung |
| contents | Stochastic models for biochemical reaction networks are widely used to explore their complex dynamics but face significant challenges, including difficulties in determining rate constants and high computational costs. To address these issues, model reduction approaches based on deterministic quasi-steady-state approximations (QSSA) have been employed, resulting in propensity functions in the form of deterministic non-elementary reaction functions, such as the Michaelis-Menten equation. In particular, the total QSSA (tQSSA), known for its accuracy in deterministic frameworks, has been perceived as universally valid for stochastic model reduction. However, recent studies have challenged this perception. In this review, we demonstrate that applying tQSSA in stochastic model reduction can distort dynamics, even in cases where the deterministic tQSSA is rigorously valid. This highlights the need for caution when using deterministic QSSA in stochastic model reduction to avoid erroneous conclusions from model simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Validity of the total quasi-steady-state approximation in stochastic biochemical reaction networks Song, Yun Min Lee, Kangmin Kim, Jae Kyoung Molecular Networks Probability Stochastic models for biochemical reaction networks are widely used to explore their complex dynamics but face significant challenges, including difficulties in determining rate constants and high computational costs. To address these issues, model reduction approaches based on deterministic quasi-steady-state approximations (QSSA) have been employed, resulting in propensity functions in the form of deterministic non-elementary reaction functions, such as the Michaelis-Menten equation. In particular, the total QSSA (tQSSA), known for its accuracy in deterministic frameworks, has been perceived as universally valid for stochastic model reduction. However, recent studies have challenged this perception. In this review, we demonstrate that applying tQSSA in stochastic model reduction can distort dynamics, even in cases where the deterministic tQSSA is rigorously valid. This highlights the need for caution when using deterministic QSSA in stochastic model reduction to avoid erroneous conclusions from model simulations. |
| title | Validity of the total quasi-steady-state approximation in stochastic biochemical reaction networks |
| topic | Molecular Networks Probability |
| url | https://arxiv.org/abs/2503.00776 |