Agent-based Models to address the Crisis of Reproducibility and Precision Medicine

基于代理的模型解决可重复性和精准医学的危机

• 批准号: 9920235
• 负责人: Gary An
• 金额: $ 56.74万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9920235
• 关键词: Address; Affect; Behavior; Behavioral; Behavioral Model; Biological; Biomedical Research; Calibration; Classification; Clinical; Clinical Trials; Data; Data Set; Development; Disease; Environment; Epigenetic Process; Evaluation; General Population; Genetic Variation; Goals; Heterogeneity; Individual; Inflammation; Intervention; Knowledge; Lead; Learning; Maps; Mathematics; Methodology; Methods; Modality; Modeling; Molecular; Organism; Outcome; Pathologic; Pathway interactions; Patient Rights; Pharmaceutical Preparations; Phenotype; Population; Process; Proxy; Psychological reinforcement; Recurrence; Reproducibility; Reproducibility of Results; Research; Running; Sampling; Space Explorations; System; Therapeutic; Time; Validation; base; biological heterogeneity; biological systems; clinical Diagnosis; design; experimental study; functional mimics; models and simulation; multi-scale modeling; novel; organizational structure; outcome forecast; pre-clinical; precision medicine; repaired; simulation

Summary This proposal seeks to address fundamental methodological challenges associated with the development and use of multi-scale models (MSMs), and by extension, can potentially address a current epistemic crisis affecting biomedical research as a whole. We propose an approach by which a novel perspective of using MSMs, and specifically agent-based models (ABMs), provides a means of explaining and eventually addressing the Crisis of Reproducibility, and, in so doing, providing a tractable path towards “real” Precision Medicine (i.e. right drug, right patient, right time, and how to design such a strategy). We assert that the Crisis of Reproducibility arises in great part because of the sparseness of “real world” data relative to the space of all possible biological/pathological phenotypes (in terms of system state and especially system trajectories); this leads to a discordance between what can be sampled experimentally and the true richness of biological heterogeneity. We further propose that addressing this discrepancy can be accomplished by approximating the behavioral landscape of a system using large-scale parameter/trajectory space exploration of ABMs as proxies for the real world system. This perspective is novel because it emphasizes the distribution and variability of multi-dimensional spaces/manifolds generated by many trajectories, as opposed to the individual or highly- selected subset of trajectories that result from classical parameter fitting/calibration. Thus, the validation target shifts away from high-fidelity/precision fitting (e.g. fitting mean values of a single dataset), which contributes to the sparseness problem; instead, validation involves recapitulating the breadth of coverage and distribution of outcomes across many datasets, which embraces heterogeneity. Given the importance of system dynamics and the non-uniqueness of trajectories to a particular state, this perspective leads to our assertions that true Precision Medicine can only be achieved after behavioral manifolds are thoroughly characterized, and that, without an existing mathematical formalism, establishing the direction for developing control strategies can best be achieved using evolutionary computing and reinforcement learning on simulation data.

概括 该建议旨在应对与发展相关的基本方法论挑战 使用多尺度模型（MSMS）并扩展，可以潜在地解决当前的认知危机 整体影响生物医学研究。我们提出了一种方法，一种新颖的观点 MSM，特别是基于代理的模型（ABMS），提供了一种解释，有时提供的方法 解决可重复性的危机，并在这样做的过程中提供了通往“真实”精确度的寓意途径 药物（即正确的药物，正确的患者，正确的时间以及如何设计这种策略）。我们断言危机 重现性的很大程度上是因为“现实世界”数据相对于所有人的空间的稀疏性 可能的生物/病理表型（就系统状态，尤其是系统轨迹而言）；这 导致可以在实验中取样的内容与生物学的真实丰富性之间的不一致 异质性。我们进一步提出，解决此差异可以通过近似来实现 使用大规模参数/轨迹空间探索ABM作为代理的系统的行为格局 对于现实世界系统。这种观点是新颖的，因为它强调了 许多轨迹产生的多维空间/流形，而不是个人或高度 由经典参数拟合/校准产生的轨迹的选定子集。那，验证目标 从高保真/精度拟合转移（例如，拟合表示单个数据集的值），这有助于 稀疏问题；相反，验证涉及概括覆盖范围和分布的广度 许多数据集的结果都包含异质性。鉴于系统动态的重要性 以及轨迹到特定状态的非唯一性，这种观点导致了我们的断言 只有在彻底表征行为歧管之后才能实现精密医学，这就是 没有现有的数学格式，建立制定控制策略的方向可以 在模拟数据上使用进化计算和加强学习可以实现最好的成绩。