title: An integrated approach for inference and mechanistic modeling for advancing drug development.
abstract: An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.
authors.affiliation: Gene Network Sciences, Inc. 31 Dutch Mill Road, Ithaca, NY 14850, USA.
authors.forename: Sergej V
authors.initials: SV
authors.lastname: Aksenov
authors.forename: Bruce
authors.initials: B
authors.lastname: Church
authors.forename: Anjali
authors.initials: A
authors.lastname: Dhiman
authors.forename: Anna
authors.initials: A
authors.lastname: Georgieva
authors.forename: Ramesh
authors.initials: R
authors.lastname: Sarangapani
authors.forename: Gabriel
authors.initials: G
authors.lastname: Helmlinger
authors.forename: Iya G
authors.initials: IG
authors.lastname: Khalil
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D015195:Drug Design; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological