Combining systems biology and big data analytics to uncover metabolic and environmental factors in autism spectrum disorder
Authors
Vargason, Troy
ORCID
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Other Contributors
Hahn, Juergen
McGuinness, Deborah L.
Intes, Xavier
Wan, Leo Q.
McGuinness, Deborah L.
Intes, Xavier
Wan, Leo Q.
Issue Date
2019-08
Keywords
Biomedical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Given the inherent complexity of ASD, reaching this goal will necessitate a deviation from traditional medical approaches, which typically focus on single physiological mechanisms individually, towards systems-oriented and big data methodologies to investigate these systems concurrently. This work will begin with the development of a mathematical model for capturing folate-dependent one-carbon metabolism and transsulfuration activity in individuals with ASD. Estimation of personalized model parameters using clinical case-control data and calculation of parameter distributions will reveal metabolic reactions where abnormalities may be present in ASD. The next area of investigation will use multivariate statistical methods to evaluate the efficacy of biochemical measurements as potential markers for diagnosing ASD and evaluating outcomes of clinical treatment. Results will be assessed in the context of the models’ abilities to predict new data that were not used during model development. Finally, a retrospective analysis of administrative medical claims data will be performed to examine patterns in the diagnosis of co-occurring conditions in children with ASD. Studying co-occurring conditions and their contributing factors offers potential for understanding the underlying mechanisms of subgroups of ASD, which can be explored in future work.
Description
August 2019
School of Engineering
School of Engineering
Department
Dept. of Biomedical Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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