title: Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.
abstract: Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.
authors.affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany; International Graduate School of Molecular Medicine, Ulm University, Ulm, Germany.
authors.forename: Melanie
authors.initials: M
authors.lastname: Grieb
authors.affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany; Neural Information Processing, Ulm University, Ulm, Germany; International Graduate School of Molecular Medicine, Ulm University, Ulm, Germany.
authors.forename: Andre
authors.initials: A
authors.lastname: Burkovski
authors.affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany.
authors.forename: J Eric
authors.initials: JE
authors.lastname: Sträng
authors.affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany.
authors.forename: Johann M
authors.initials: JM
authors.lastname: Kraus
authors.affiliation: Leibniz Institute for Age Research, Fritz-Lipmann Institute, Jena, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Groß
authors.affiliation: Neural Information Processing, Ulm University, Ulm, Germany.
authors.forename: Günther
authors.initials: G
authors.lastname: Palm
authors.affiliation: Institute for Biochemistry and Molecular Biology, Ulm University, Ulm, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Kühl
authors.affiliation: Leibniz Institute for Age Research, Fritz-Lipmann Institute, Jena, Germany; Core Unit Medical Systems Biology, Ulm University, Ulm, Germany; Neural Information Processing, Ulm University, Ulm, Germany.
authors.forename: Hans A
authors.initials: HA
authors.lastname: Kestler
mesh_terms: D000465:Algorithms; D000818:Animals; D015870:Gene Expression; D053263:Gene Regulatory Networks; D008955:Models, Cardiovascular; D008957:Models, Genetic; D009206:Myocardium / Q000378:metabolism*; D015398:Signal Transduction / Q000235:genetics; D035501:Uncertainty; D014981:Xenopus / Q000235:genetics; D029867:Xenopus Proteins / Q000235:genetics
chemical_list: D029867:Xenopus Proteins