Jin Ziyan, Zhou Xinyi, Fang Zhaoyuan
Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital, and Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
PLoS Comput Biol. 2025 Apr 8;21(4):e1012919. doi: 10.1371/journal.pcbi.1012919. eCollection 2025 Apr.
Stochastic Simulation Algorithm (SSA) is crucial for modeling biochemical reactions and gene regulatory networks. Traditional SSA is characterized by Markovian property and cannot naturally model systems with time delays. Several algorithms have already been designed to handle delayed reactions, yet few easy-to-use implementations exist. To address these challenges, we have developed DelaySSA, an R package that implements currently available algorithms for SSA with or without delays. Meanwhile, we also provided Matlab and Python versions to support wider applications. We demonstrated its accuracy and validity by simulating two classical models: the Bursty model and Refractory model. We then tested its capability to simulate the RNA Velocity model, where it successfully reproduced both the up- and down-regulation stages in the phase portrait. Finally, we extended its application to simulate a gene regulatory network of lung cancer adeno-to-squamous transition (AST) and qualitatively analyzed its bistability behavior by approximating the Waddington's landscape. Modeling the therapeutic intervention of a SOX2 degrader as a delayed degradation reaction, AST is effectively blocked and reprogrammed back to the adenocarcinoma state, providing a useful clue for targeting drug-resistant AST in the future. Taken together, DelaySSA is a powerful and easy-to-use software suite, facilitating accurate modeling of various kinds of biological systems and broadening the scope of stochastic simulations in systems biology.
随机模拟算法（SSA）对于生化反应和基因调控网络的建模至关重要。传统的SSA具有马尔可夫性质，无法自然地对具有时间延迟的系统进行建模。已经设计了几种算法来处理延迟反应，但易于使用的实现却很少。为了应对这些挑战，我们开发了DelaySSA，这是一个R包，它实现了目前可用的有或无延迟的SSA算法。同时，我们还提供了Matlab和Python版本以支持更广泛的应用。我们通过模拟两个经典模型：爆发模型和不应期模型，证明了它的准确性和有效性。然后我们测试了它模拟RNA速度模型的能力，它成功地在相图中重现了上调和下调阶段。最后，我们将其应用扩展到模拟肺癌腺鳞癌转化（AST）的基因调控网络，并通过近似沃丁顿景观对其双稳态行为进行了定性分析。将SOX2降解剂的治疗干预建模为延迟降解反应，AST被有效地阻断并重新编程回腺癌状态，为未来靶向耐药性AST提供了有用的线索。综上所述，DelaySSA是一个功能强大且易于使用的软件套件，有助于对各种生物系统进行准确建模，并拓宽了系统生物学中随机模拟的范围。
