Modeling and controlling uncertainty in multi-level biological systems

Mathematical modeling is essential to the understanding and design of biological systems. Modeling uncertainty, which variously represents lack of data, variability between individuals and between different measurements of a single individual, ambiguity in the proper model form, and others, is essential to explaining the limitations of our understanding and constraining the confidence of our predictions. Current methods for modeling uncertainty provide a rich mathematical means of analyzing simple forms of uncertainty in self-contained models. However, real biological systems of interest exhibit many forms of uncertainty simultaneously and may require the composition of multiple levels of models to create useful predictions. I develop and test new methods for characterizing and propagating uncertainty through multi-level models in order to better make clinically relevant predictions. These methods are applied to three systems.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Biological Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Shi, Kevin,Ph. D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Bruce Tidor.

• Biological Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121701

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121701