Department of Mathematics, Virginia Tech, Blacksburg, VA, United States of America.
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States of America.
PLoS One. 2021 May 21;16(5):e0251926. doi: 10.1371/journal.pone.0251926. eCollection 2021.
In many physiological systems, real-time endogeneous and exogenous signals in living organisms provide critical information and interpretations of physiological functions; however, these signals or variables of interest are not directly accessible and must be estimated from noisy, measured signals. In this paper, we study an inverse problem of recovering gas exchange signals of animals placed in a flow-through respirometry chamber from measured gas concentrations. For large-scale experiments (e.g., long scans with high sampling rate) that have many uncertainties (e.g., noise in the observations or an unknown impulse response function), this is a computationally challenging inverse problem. We first describe various computational tools that can be used for respirometry reconstruction and uncertainty quantification when the impulse response function is known. Then, we address the more challenging problem where the impulse response function is not known or only partially known. We describe nonlinear optimization methods for reconstruction, where both the unknown model parameters and the unknown signal are reconstructed simultaneously. Numerical experiments show the benefits and potential impacts of these methods in respirometry.
在许多生理系统中，生物体内实时的内源性和外源性信号提供了对生理功能的关键信息和解释；然而，这些感兴趣的信号或变量不能直接获取，必须从噪声测量信号中进行估计。在本文中，我们研究了一个从测量的气体浓度中恢复置于流动呼吸计室中的动物的气体交换信号的反问题。对于具有许多不确定性的大规模实验（例如，具有高采样率的长时间扫描），这是一个具有计算挑战性的反问题。我们首先描述了在知道脉冲响应函数时可用于呼吸计重建和不确定性量化的各种计算工具。然后，我们解决了更具挑战性的问题，其中脉冲响应函数未知或仅部分已知。我们描述了用于重建的非线性优化方法，其中同时重建未知的模型参数和未知信号。数值实验表明了这些方法在呼吸计中的益处和潜在影响。
