College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
Biosensors (Basel). 2024 Aug 5;14(8):378. doi: 10.3390/bios14080378.
Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.
植物已经进化出复杂的信号通路，这些通路作为由反馈控制的网络运作，以应对胁迫。然而，这些途径背后复杂的分子机制仍需要被理解，而实验验证则带来了巨大的挑战和费用。因此，计算假说评估在理解植物信号动力学方面变得越来越重要。生物传感器经过基因改造，当暴露于特定激素（如脱落酸 (ABA)）时会发出光，从而实现定量。我们开发了计算模型，利用 Hill 方程和常微分方程 (ODE) 来模拟 ABA 浓度与生物发光传感器之间的关系，这有助于更好地发展关于植物信号的假说。基于模拟结果，在给定特定参数和模型假设的情况下，对于 1.5 μmol 的浓度，记录了 47.646 RLUs 的发光强度。该方法增强了我们对细胞水平植物信号通路的理解，以具有成本效益的方式为科学界带来了巨大的益处。这些计算预测与实验结果的一致性强调了我们方法的稳健性，为通过实验验证数学模型提供了一种具有成本效益的手段。该研究旨在将生物传感器的生物发光与植物信号及其数学模型相关联，以实现对特定植物激素 ABA 的定量检测。
