Sengupta Dola, Kar Sandip
Department of Chemistry, IIT Bombay, Powai, Mumbai - 400076, India.
PLoS One. 2015 Sep 1;10(9):e0136668. doi: 10.1371/journal.pone.0136668. eCollection 2015.
Large gene regulatory networks (GRN) are often modeled with quasi-steady-state approximation (QSSA) to reduce the huge computational time required for intrinsic noise quantification using Gillespie stochastic simulation algorithm (SSA). However, the question still remains whether the stochastic QSSA model measures the intrinsic noise as accurately as the SSA performed for a detailed mechanistic model or not? To address this issue, we have constructed mechanistic and QSSA models for few frequently observed GRNs exhibiting switching behavior and performed stochastic simulations with them. Our results strongly suggest that the performance of a stochastic QSSA model in comparison to SSA performed for a mechanistic model critically relies on the absolute values of the mRNA and protein half-lives involved in the corresponding GRN. The extent of accuracy level achieved by the stochastic QSSA model calculations will depend on the level of bursting frequency generated due to the absolute value of the half-life of either mRNA or protein or for both the species. For the GRNs considered, the stochastic QSSA quantifies the intrinsic noise at the protein level with greater accuracy and for larger combinations of half-life values of mRNA and protein, whereas in case of mRNA the satisfactory accuracy level can only be reached for limited combinations of absolute values of half-lives. Further, we have clearly demonstrated that the abundance levels of mRNA and protein hardly matter for such comparison between QSSA and mechanistic models. Based on our findings, we conclude that QSSA model can be a good choice for evaluating intrinsic noise for other GRNs as well, provided we make a rational choice based on experimental half-life values available in literature.
大型基因调控网络（GRN）通常采用准稳态近似（QSSA）进行建模，以减少使用 Gillespie 随机模拟算法（SSA）进行内在噪声量化所需的巨大计算时间。然而，随机 QSSA 模型测量内在噪声的准确性是否与针对详细机制模型执行的 SSA 一样，这个问题仍然存在。为了解决这个问题，我们为一些经常观察到的表现出开关行为的 GRN 构建了机制模型和 QSSA 模型，并对它们进行了随机模拟。我们的结果强烈表明，与针对机制模型执行的 SSA 相比，随机 QSSA 模型的性能严重依赖于相应 GRN 中涉及的 mRNA 和蛋白质半衰期的绝对值。随机 QSSA 模型计算所达到的准确程度将取决于由于 mRNA 或蛋白质或两者的半衰期绝对值所产生的爆发频率水平。对于所考虑的 GRN，随机 QSSA 在蛋白质水平上更准确地量化内在噪声，并且对于 mRNA 和蛋白质半衰期值的更大组合也是如此，而在 mRNA 的情况下，只有在半衰期绝对值的有限组合下才能达到令人满意的准确程度。此外，我们已经清楚地证明，mRNA 和蛋白质的丰度水平对于 QSSA 和机制模型之间的这种比较几乎没有影响。基于我们的发现，我们得出结论，只要我们根据文献中可用的实验半衰期值做出合理选择，QSSA 模型也可以是评估其他 GRN 内在噪声的一个不错选择。
