A systems biology network model for genetic association studies of nicotine addiction and treatment.

ABSTRACT:

Objective

Interpreting genome-scale genetic association data, particularly for complex diseases and phenotypes, requires extensive use of prior knowledge across a broad range of potential biological and environmental influences, spanning many scientific subdisciplines. We suggest that known or hypothesized disease risk factors, and causal mechanisms, can be represented using an ontology, a computational specification of a set of concepts and the relations between them.

Methods

We have integrated the expertise of multiple investigators in nicotine pharmacokinetics and pharmacodynamics, nicotine dependence, and clinical smoking cessation outcomes, and represented this knowledge in an ontology-based network model. Our model spans multiple scales, from molecules, genes and cellu

SUBMITTER: Thomas PD 

PROVIDER: S-EPMC6485245 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature