Computational Modeling Under Uncertainty: Challenges and Opportunities

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Computational Biology has increasingly become an important tool for biomedical and translational research. In particular, when generating novel hypothesis despite fundamental uncertainties in data and mechanistic understanding of biological processes underpinning diseases. While in the present book, we have reviewed the necessary background and existing novel methodologies that set the basis for dealing with uncertainty, there are still many “grey”, or less well-defined, areas of investigations offering both challenges and opportunities. This final chapter in the book provides some reflections on those areas, namely: (1) the need for novel robust mathematical and statistical methodologies to generate hypothesis under uncertainty; (2) the challenge of aligning those methodologies in a context that requires larger computational resources; (3) the accessibility of modeling tools for less mathematical literate researchers; and (4) the integration of models with—omics data and its application in clinical environments.
Original languageEnglish (US)
Title of host publicationStudies in Mechanobiology, Tissue Engineering and Biomaterials
PublisherSpringer
Pages467-476
Number of pages10
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

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