title: Omics approaches to individual variation: modeling networks and the virtual patient.
abstract: Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment-a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on "virtual patient" models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available. Depending on the disease, its mechanisms, and the therapy, virtual patient models can be implemented at a fairly high level of abstraction, with molecular models representing cells, cell types, or organs relevant to the clinical question, interacting not only with each other but also the environment. In the future, "virtual patient/in-silico self" models may not only become a central element of our health care system, reducing otherwise unavoidable mistakes and unnecessary costs, but also act as "guardian angels" accompanying us through life to protect us against dangers and to help us to deal intelligently with our own health and wellness.
authors.affiliation: Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany; Dahlem Centre for Genome Research and Medical Systems Biology (DCGMS), Berlin, Germany; French National Institute of Health and Medical Research (INSERM), US13, Paris, France; Alacris Theranostics GmbH, Berlin, Germany.
authors.forename: Hans
authors.initials: H
authors.lastname: Lehrach
mesh_terms: D003198:Computer Simulation; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D057285:Precision Medicine
keywords: cancer; drug response; mechanistic model; next-generation sequencing; patient model; virtual clinical trial