Teichner Ron, Brenner Naama, Meir Ron
Viterbi Department of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Network Biology Research Laboratory, Technion - Israel Institute of Technology, Haifa, Israel.
PLoS One. 2025 Jun 5;20(6):e0325443. doi: 10.1371/journal.pone.0325443. eCollection 2025.
Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectives are time-varying, and require the regulation system to follow a dynamic set-point. For example, the sleep-wake cycle varies according to the 24 hours solar day, inducing oscillatory dynamics on the regulation set-point; nutrient availability fluctuates in the organism, inducing time-varying set-points for metabolism. In this work, we introduce a novel data-driven algorithm capable of identifying internal regulation objectives that are maintained with respect to a dynamic reference value. This builds on a previous algorithm that identified variables regulated with respect to fixed set-point values. The new algorithm requires adding a prediction component that not only identifies the internally regulated variables, but also predicts the dynamic set-point as part of the process. To the best of our knowledge, this is the first algorithm that is able to achieve this. We test the algorithm on simulation data from realistic biological models, demonstrating excellent empirical results.
生物系统尽管受到外部和内部干扰，仍能维持其功能的稳定性。研究生物调节的一个重要挑战是根据经验数据确定控制目标。这些目标通常是随时间变化的，需要调节系统跟踪动态设定点。例如，睡眠-觉醒周期根据24小时的太阳日变化，在调节设定点上产生振荡动力学；生物体中的营养物质可用性波动，导致新陈代谢的设定点随时间变化。在这项工作中，我们引入了一种新颖的数据驱动算法，该算法能够识别相对于动态参考值保持的内部调节目标。这建立在先前一种识别相对于固定设定点值进行调节的变量的算法之上。新算法需要添加一个预测组件，该组件不仅能识别内部调节变量，还能将动态设定点作为过程的一部分进行预测。据我们所知，这是第一种能够实现这一点的算法。我们在来自现实生物模型的模拟数据上测试了该算法，展示了出色的实验结果。
