Lei Shicong, Li Yu'an, Ma Zheng, Zhang Hepeng, Tang Min
School of Mathematics, Shanghai Jiao Tong University, Shanghai, China.
Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
PLoS Comput Biol. 2025 Aug 5;21(8):e1013287. doi: 10.1371/journal.pcbi.1013287. eCollection 2025 Aug.
The run-and-tumble behavior is a simple yet powerful mechanism that enables microorganisms to efficiently navigate and adapt to their environment. These organisms run and tumble alternately, with transition rates modulated by intracellular chemical concentration. We introduce a neural network-based model capable of identifying the governing equations underlying run-and-tumble dynamics. This model accommodates the nonlinear functions describing movement responses to intracellular biochemical reactions by integrating the general structure of ODEs that represent these reactions, without requiring explicit reconstruction of the reaction mechanisms. It is trained on datasets of measured responses to simple, controllable signals. The resulting model is capable of predicting movement responses in more realistic, complex, temporally varying environments. Moreover, the model can be used to deduce the underlying structure of hidden intracellular biochemical dynamics. We have successfully tested the validity of the identified equations based on various models of Escherichia coli chemotaxis, demonstrating efficacy even in the presence of noisy measurements. Moreover, we have identified the governing equation of the photo-response of Euglena gracilis cells using experimental data, which was previously unknown, and predicted the potential architecture of the intracellular photo-response pathways for these cells.
“奔跑-翻滚”行为是一种简单却强大的机制，能使微生物有效地在环境中导航并适应环境。这些生物体交替进行奔跑和翻滚，其转换速率由细胞内化学物质浓度调节。我们引入了一种基于神经网络的模型，该模型能够识别“奔跑-翻滚”动力学背后的控制方程。通过整合表示这些反应的常微分方程（ODE）的一般结构，此模型容纳了描述对细胞内生化反应运动响应的非线性函数，而无需明确重建反应机制。它在对简单、可控信号的测量响应数据集上进行训练。所得模型能够预测在更现实、复杂且随时间变化的环境中的运动响应。此外，该模型可用于推断隐藏的细胞内生化动力学的潜在结构。我们基于大肠杆菌趋化性的各种模型成功测试了所识别方程的有效性，即使在存在噪声测量的情况下也证明了其有效性。此外，我们利用实验数据确定了纤细裸藻细胞光响应的控制方程，该方程此前未知，并预测了这些细胞内光响应途径的潜在结构。
