Practical challenges for biomedical modeling using HPC
- Published
- Accepted
- Subject Areas
- Computational Biology, Scientific Computing and Simulation
- Keywords
- HPC, Biomedical simulation
- Copyright
- © 2018 Wright et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Practical challenges for biomedical modeling using HPC. PeerJ Preprints 6:e27299v1 https://doi.org/10.7287/peerj.preprints.27299v1
Abstract
The concept underlying precision medicine is that prevention, diagnosis and treatment of pathologies such as cancer can be improved through an understanding of the influence of individual patient characteristics. Predictive medicine seeks to derive this understanding through mechanistic models of the causes and (potential) progression of diseases within a given individual. This represents a grand challenge for computational biomedicine as it requires the integration of highly varied (and potentially vast) quantitative experimental datasets into models of complex biological systems. It is becoming increasingly clear that this challenge can only be answered through the use of complex workflows that combine diverse analyses and whose design is informed by an understanding of how predictions must be accompanied by estimates of uncertainty. Each stage in such a workflow can, in general, have very different computational requirements. If funding bodies and the HPC community are serious about the desire to support such approaches, they must consider the need for portable, persistent and stable tools designed to promote extensive long term development and testing of these workflows. From the perspective of model developers (and with even greater relevance to potential clinical or experimental collaborators) the enormous diversity of interfaces and supercomputer policies, frequently designed with monolithic applications in mind, can represent a serious barrier to innovation. Here we use experiences from work on two very different biomedical modeling scenarios - brain bloodflow and small molecule drug selection - to highlight issues with the current programming and execution environments and suggest potential solutions.
Author Comment
In this article we describe some of the challenges faced in bringing the types of simulations used in biomedical research to HPC environments and making them accesible to researchers in the field.
It was originally submitted to and presented at the Second HPC Applications in Precision Medicine Workshop held at the ISC High Performance conference (ISC2018).