title: Estimations of intrinsic and extrinsic noise in models of nonlinear genetic networks.
abstract: We discuss two methods that can be used to estimate the impact of internal and external variability on nonlinear systems, and demonstrate their utility by comparing two experimentally implemented oscillatory genetic networks with different designs. The methods allow for rapid estimations of intrinsic and extrinsic noise and should prove useful in the analysis of natural genetic networks and when constructing synthetic gene regulatory systems.
authors.affiliation: Center for Theoretical Biological Physics, Department of Physics, University of California, San Diego, La Jolla, California, 92093-0374, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Scott
authors.forename: Brian
authors.initials: B
authors.lastname: Ingalls
authors.forename: Mads
authors.initials: M
authors.lastname: Kaern
mesh_terms: D000818:Animals; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors