Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

IF 2 4区 数学 Q2 BIOLOGY
Nicholas N Lam, Rua Murray, Paul D Docherty
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Abstract

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Abstract Image

利用遗传算法与轮廓-似然度量,改进剂量-反应模型的取样方案。
数据质量和数量的实际限制会限制数学模型参数识别的精确性。为了最大限度地减少参数的不确定性,人们开发了基于模型的实验设计方法,但这些方法大多依赖于参数空间局部点模型灵敏度的一阶近似值。实际的可识别性方法(如轮廓似然法)已显示出超越线性近似方法量化参数不确定性的潜力。本研究提出了一种遗传算法方法,用于优化示范 PK-PD 模型各种参数化的采样时间,目的是辅助实验设计。优化依赖于所选的参数不确定性度量,该度量基于轮廓似然法。此外,该方法还考虑了可能需要同时优化多个参数方案的情况。遗传算法方法能够在广泛的样本数(n = 3-20)范围内找到接近最优的取样方案,并将参数方差指标平均降低 33-37%。轮廓似然指标与现有的基于蒙特卡罗的指标也有很好的相关性(最坏情况下 r > 0.89),同时将计算成本降低了一个数量级。新的轮廓似然度量和遗传算法的结合证明了在优化实验设计中以合理的计算成本考虑模型非线性性质的可行性。这一过程的结果可以让实验人员在样本数量固定的情况下提高参数的确定性,或者在保持参数确定性水平不变的情况下减少样本数量。
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来源期刊
CiteScore
3.90
自引率
8.60%
发文量
123
审稿时长
7.5 months
期刊介绍: The Bulletin of Mathematical Biology, the official journal of the Society for Mathematical Biology, disseminates original research findings and other information relevant to the interface of biology and the mathematical sciences. Contributions should have relevance to both fields. In order to accommodate the broad scope of new developments, the journal accepts a variety of contributions, including: Original research articles focused on new biological insights gained with the help of tools from the mathematical sciences or new mathematical tools and methods with demonstrated applicability to biological investigations Research in mathematical biology education Reviews Commentaries Perspectives, and contributions that discuss issues important to the profession All contributions are peer-reviewed.
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