title: Exploiting Natural Fluctuations to Identify Kinetic Mechanisms in Sparsely Characterized Systems.
abstract: From biochemistry to ecology, many biological systems are stochastic, complex, and sparsely characterized. In such systems, each component may respond to changes in any directly or indirectly connected components, thus requiring knowledge of the whole to predict the dynamics of the parts. Here, we address this challenge by deriving relations between properties of fluctuations that only reflect local interactions between a subset of components but are invariant to all indirectly connected dynamics. This greatly reduces the number of assumptions when evaluating dynamic models experimentally. We illustrate the approach by revisiting systematic single-cell gene expression data, and we show that the observed fluctuations contradict the assumptions made in most published models of stochastic gene expression, even when accounting for the possibility of systematic experimental artifacts.
authors.affiliation: Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA. Electronic address: andreas_hilfinger@hms.harvard.edu.
authors.forename: Andreas
authors.initials: A
authors.lastname: Hilfinger
authors.affiliation: Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Norman
authors.affiliation: Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA. Electronic address: johan_paulsson@harvard.edu.
authors.forename: Johan
authors.initials: J
authors.lastname: Paulsson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004463:Ecology; D053263:Gene Regulatory Networks; D007700:Kinetics; D008954:Models, Biological; D013269:Stochastic Processes
keywords: gene expression; single-cell dynamics; stochastic processes; theorems in biology