Systems Genomics, by one definition, is “an analysis approach that models the complex inter- and intra-individual variations of traits and diseases using data from next-generation omic data”. This typically involves:

  1. integrating several layers of information on the genome, epigenome, transcriptome, proteome or metabolome, measured in a large number of individuals, cell types or experimental conditions, and
  2. identifying the key molecular components and control mechanisms that can influence cells or whole organisms to achieve a desired phenotypic goal (such as going from a disease to a healthy state).

(Figure reused by permission from Nature Publishing Group: Nature Reviews Genetics 16, 85 (Ritchie et al.), copyright (2015))

Our preferred approach for tackling this challenge is network inference (wikipedia link), the statistical process of identifying the most likely causal interactions between molecular components from noisy experimental data.