title: Inverse problems from biomedicine: inference of putative disease mechanisms and robust therapeutic strategies.
abstract: Many complex diseases that are difficult to treat cannot be mapped onto a single cause, but arise from the interplay of multiple contributing factors. In the study of such diseases, it is becoming apparent that therapeutic strategies targeting a single protein or metabolite are often not efficacious. Rather, a systems perspective describing the interaction of physiological components is needed. In this paper, we demonstrate via examples of disease models the kind of inverse problems that arise from the need to infer disease mechanisms and/or therapeutic strategies. We identify the challenges that arise, in particular the need to devise strategies that are robust against variable physiological states and parametric uncertainties.
authors.affiliation: Biomolecular Signaling and Control Group, Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland. james.lu.jl1@roche.com
authors.forename: James
authors.initials: J
authors.lastname: Lu
authors.forename: Elias
authors.initials: E
authors.lastname: August
authors.forename: Heinz
authors.initials: H
authors.lastname: Koeppl
mesh_terms: D003480:Cushing Syndrome / Q000209:etiology / Q000503:physiopathology; D004194:Disease / Q000209:etiology*; D066246:ErbB Receptors / Q000235:genetics / Q000378:metabolism; D006801:Humans; D007030:Hypothalamo-Hypophyseal System / Q000503:physiopathology; D008074:Lipoproteins / Q000378:metabolism; D020935:MAP Kinase Signaling System; D055641:Mathematical Concepts; D008954:Models, Biological; D009154:Mutation; D010913:Pituitary-Adrenal System / Q000503:physiopathology; D049490:Systems Biology; D013812:Therapeutics / Q000706:statistics & numerical data*
chemical_list: D008074:Lipoproteins; D066246:ErbB Receptors