From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation
Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, thi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025000029 |
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| author | Seok Joo Chae Seolah Shin Kangmin Lee Seunggyu Lee Jae Kyoung Kim |
| author_facet | Seok Joo Chae Seolah Shin Kangmin Lee Seunggyu Lee Jae Kyoung Kim |
| author_sort | Seok Joo Chae |
| collection | DOAJ |
| description | Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity. |
| format | Article |
| id | doaj-art-9ebc0b57a00c46afa6defe0af4ac02ac |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-9ebc0b57a00c46afa6defe0af4ac02ac2025-01-22T05:41:40ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127411422From homogeneity to heterogeneity: Refining stochastic simulations of gene regulationSeok Joo Chae0Seolah Shin1Kangmin Lee2Seunggyu Lee3Jae Kyoung Kim4Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Bioengineering, Rice University, Houston, 77005, TX, United States of AmericaBiomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Applied Mathematics, Korea University, Seoul, 02841, Republic of KoreaDepartment of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of KoreaBiomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Applied Mathematics, Korea University, Seoul, 02841, Republic of Korea; Corresponding authors at: Biomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Medicine, College of Medicine, Korea University, Seoul, 02841, Republic of Korea; Corresponding authors at: Biomedical Mathematics group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity.http://www.sciencedirect.com/science/article/pii/S2001037025000029 |
| spellingShingle | Seok Joo Chae Seolah Shin Kangmin Lee Seunggyu Lee Jae Kyoung Kim From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation Computational and Structural Biotechnology Journal |
| title | From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation |
| title_full | From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation |
| title_fullStr | From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation |
| title_full_unstemmed | From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation |
| title_short | From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation |
| title_sort | from homogeneity to heterogeneity refining stochastic simulations of gene regulation |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025000029 |
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