Chae Seok Joo, Shin Seolah, Lee Kangmin, Lee Seunggyu, Kim Jae Kyoung
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.
Comput Struct Biotechnol J. 2025 Jan 15;27:411-422. doi: 10.1016/j.csbj.2025.01.004. eCollection 2025.
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.
细胞过程通过基因调控得到精细控制，而由于涉及的分子数量较少，内在噪声对基因调控有显著影响。吉莱斯皮算法是一种广泛使用的随机模拟方法，被普遍用于对这些系统进行建模。然而，该算法通常假定DNA在整个细胞核中均匀分布，这并不现实。在本研究中，我们评估了基于空间均匀性假设的随机模拟是否能够准确捕捉基因调控的动态变化。我们的研究结果表明，当转录因子扩散缓慢时，这些模拟无法准确捕捉基因表达，这凸显了考虑空间异质性的必要性。然而，纳入空间异质性会显著增加计算时间。为了解决这个问题，我们探索了各种随机准稳态近似（QSSA）方法，这些方法可以简化模型并减少模拟时间。虽然随机全准稳态近似（stQSSA）和随机低态准稳态近似（slQSSA）都减少了模拟时间，但只有slQSSA提供了准确的模型简化。我们的研究强调了使用适当方法进行高效、准确的基因调控动态随机模拟的重要性，特别是在纳入空间异质性时。
