title: On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.
abstract: One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.
authors.affiliation: Okayama University, Biomedical Engineering Laboratory, 3-1-1 Tsushimanaka, Kita-ku 700-8530, Japan; Universidad Politécnica de Madrid, José Gutiérrez Abascal, 2, Madrid 28006, Spain. Electronic address: jd.gomez@upm.es.
authors.forename: Jaime
authors.initials: J
authors.lastname: Gomez-Ramirez
authors.forename: Ricardo
authors.initials: R
authors.lastname: Sanz
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D008433:Mathematics; D008954:Models, Biological; D015233:Models, Statistical; D008967:Molecular Biology / Q000379:methods; D049490:Systems Biology / Q000379:methods*; D018511:Systems Integration
keywords: Bayesian inference; Cell centric perspective; Full Bayesian approach; Inverse problem; Mathematical biology; Probability distributions conditional on biophysical information