Mechanistic analysis of challenge-response experiments.
Mechanistic analysis of challenge-response experiments.
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DOI：
10.1111/biom.12066
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发表时间：
2013-09
期刊：
影响因子：
1.9
通讯作者：
Wikswo JP
中科院分区：
文献类型：
--
作者：
Shotwell MS;Drake KJ;Sidorov VY;Wikswo JP
我们提出了机械建模和非线性纵向回归在生物医学对挑战的反应实验的背景下的应用，这是一个这些方法没有得到充分利用的领域。在这种类型的实验中，通过施加实验挑战，然后观察其响应来研究系统。机械建模和非线性纵向回归的结合带来了新的洞察力，揭示了优化设计的意想不到的机会。具体地说，我们方法的机械方面使得实验挑战特征(例如，强度、持续时间)的优化设计成为可能。本文为这种方法奠定了一些基础。我们考虑了一系列实验，在这些实验中，离体兔的心脏受到间歇性缺氧的挑战。心脏对挑战开始做出反应，并在挑战结束时恢复。平均反应由描述心脏对缺氧挑战反应的候选机制的微分方程组模拟。心脏系统在受到挑战时比在休息时表现得更不稳定。因此，这个实验产生的观测结果显示出复杂的异方差和中心趋势的急剧变化。我们提出的证据表明，渐近的统计推断策略可能不能充分解释统计的不确定性。对两种可供选择的方法进行了定性评价(即在当前背景下的实用性)，并使用创新的蒙特卡罗方法进行了定量评价。最后，我们讨论了在对挑战的响应实验的优化设计中令人兴奋的机会。
We present an application of mechanistic modeling and nonlinear longitudinal regression in the context of biomedical response-to-challenge experiments, a field where these methods are underutilized. In this type of experiment, a system is studied by imposing an experimental challenge, and then observing its response. The combination of mechanistic modeling and nonlinear longitudinal regression has brought new insight, and revealed an unexpected opportunity for optimal design. Specifically, the mechanistic aspect of our approach enables the optimal design of experimental challenge characteristics (e.g., intensity, duration). This article lays some groundwork for this approach. We consider a series of experiments wherein an isolated rabbit heart is challenged with intermittent anoxia. The heart responds to the challenge onset, and recovers when the challenge ends. The mean response is modeled by a system of differential equations that describe a candidate mechanism for cardiac response to anoxia challenge. The cardiac system behaves more variably when challenged than when at rest. Hence, observations arising from this experiment exhibit complex heteroscedasticity and sharp changes in central tendency. We present evidence that an asymptotic statistical inference strategy may fail to adequately account for statistical uncertainty. Two alternative methods are critiqued qualitatively (i.e., for utility in the current context), and quantitatively using an innovative Monte Carlo method. We conclude with a discussion of the exciting opportunities in optimal design of response-to-challenge experiments.
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影响因子：
6.1
作者：
Faley S;Seale K;Hughey J;Schaffer DK;VanCompernolle S;McKinney B;Baudenbacher F;Unutmaz D;Wikswo JP
通讯作者：
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影响因子：
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作者：
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影响因子：
4.5
作者：
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影响因子：
2.3
作者：
Enders JR;Marasco CC;Kole A;Nguyen B;Sevugarajan S;Seale KT;Wikswo JP;McLean JA
通讯作者：
McLean JA
影响因子：
1.7
作者：
Nobre, Juvencio Santos;Singer, Julio da Motta
通讯作者：
Singer, Julio da Motta