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

总结 本建议旨在解决与发展和 使用多尺度模型（MSM），并通过扩展，可以潜在地解决当前的认识危机 影响整个生物医学研究。我们提出了一种方法，通过这种方法， MSM，特别是基于代理的模型（ABM），提供了一种解释和最终 解决生殖危机，并在这样做的过程中，提供了一条通往“真实的”精确的易处理的道路 医学（即正确的药物，正确的病人，正确的时间，以及如何设计这样的策略）。我们认为，危机 在很大程度上，由于“真实的世界”数据相对于所有空间的稀疏性， 可能的生物学/病理学表型（就系统状态，尤其是系统轨迹而言）;这 导致了实验采样与生物多样性的真实丰富性之间的不一致， 异质性我们进一步提出，解决这一矛盾可以通过近似 以反弹道导弹的大规模参数/轨迹空间探测为代理的系统行为景观 对于真实的世界体系。这种观点是新颖的，因为它强调了 由许多轨迹产生的多维空间/流形，而不是单个或高度- 从经典参数拟合/校准产生的轨迹的选定子集。因此，验证目标 偏离高保真/精确拟合（例如，拟合单个数据集的平均值），这有助于 稀疏性问题;相反，验证涉及概括覆盖范围的广度和分布， 许多数据集的结果，其中包括异质性。鉴于系统动力学的重要性 以及到达特定状态的轨迹的非唯一性，这种观点导致我们断言， 精准医疗只有在行为流形被彻底表征之后才能实现，而且， 在没有现有的数学形式的情况下，确定控制策略的发展方向， 最好使用进化计算和对模拟数据的强化学习来实现。

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.