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.

Public Health Relevance

This proposal seeks to address fundamental methodological challenges associated with the development and use of multi-scale models (MSMs), and specifically agent-based models (ABMs), to provide 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 propose that this 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,, and that, without an existing mathematical formalism, developing control strategies for true Precision Medicine can best be achieved using evolutionary computing and reinforcement learning on simulation data.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01EB025825-03
Application #
9788445
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Peng, Grace
Project Start
2018-09-19
Project End
2022-06-30
Budget Start
2020-07-01
Budget End
2021-06-30
Support Year
3
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Vermont & St Agric College
Department
Surgery
Type
Schools of Medicine
DUNS #
066811191
City
Burlington
State
VT
Country
United States
Zip Code
05405