Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models.
Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models.
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DOI：
10.1177/0037549720975075
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
An G
中科院分区：
文献类型：
--
作者：
Cockrell C;Ozik J;Collier N;An G
越来越多的人对使用基于机制的多尺度计算模型（如基于代理的模型（ABM））来生成模拟的临床人群，以发现和评估潜在的诊断和治疗方式感兴趣。生物医学模拟运行的环境描述（模型上下文）和内部模型规则的参数化（模型内容）需要优化大量的自由参数。在这项工作中，我们利用嵌套式主动学习（AL）工作流程来有效地参数化和情境化用于检查脓毒症的全身炎症的ABM。使用模型规则集之外的四个参数检查上下文参数空间。该模型的内部参数化，代表基因表达和相关的细胞行为，通过增强或抑制与炎症和伤口愈合相关的12种信号传导介质的信号传导途径进行了探索。我们已经实现了一个嵌套的AL方法，其中临床相关的（CR）模型环境空间为一个给定的内部模型参数化映射使用一个小的人工神经网络（ANN）。外部AL级工作流程是一个更大的ANN，其使用AL来有效地回归由单个内部参数化给出的CR空间的体积和质心位置。我们已经将有效映射该模型的CR参数空间所需的模拟次数减少了约99%。此外，我们已经证明，具有更多变量的更复杂的模型可能会进一步提高效率。
There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.
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