Network Methods for Elucidating the Complexity of Common Human Diseases

Abstract

As big data comes to define the landscape in the life and biomedical sciences, carrying out successful research or providing the most appropriate diagnosis and treatment path for a given patient will more and more come to depend upon one’s ability to master the big data, to derive meaning and understanding from it. We can now score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in entire populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. Most common forms of human disease such as schizophrenia, Alzheimer’s disease, and autism are not the result of single changes to single genes but rather emergent properties of the complex networks that define our systems at multiple scales (molecular, cellular, tissue, organ, organism, community). In order to elucidate the complexity of common human disease and other complex phenotypes, biological systems such as ourselves must be modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of conditions. Leveraging the vast mountains of data to model biological systems in this way demands the development of analytical approaches that simultaneously integrate different dimensions of data. Here I motivate the need to take a more holistic approach to modeling biological systems and then detail different approaches that have been recently developed to carry out this type of modeling, from leveraging DNA variation as a systematic perturbation source to infer causal relationships among traits of interest, to the application of more advanced Bayesian network reconstruction algorithms to construct predictive network models of disease.