title: Noise in genetic and neural networks.
abstract: Both neural and genetic networks are significantly noisy, and stochastic effects in both cases ultimately arise from molecular events. Nevertheless, a gulf exists between the two fields, with researchers in one often being unaware of similar work in the other. In this Special Issue, we focus on bridging this gap and present a collection of papers from both fields together. For each field, the networks studied range from just a single gene or neuron to endogenous networks. In this introductory article, we describe the sources of noise in both genetic and neural systems. We discuss the modeling techniques in each area and point out similarities. We hope that, by reading both sets of papers, ideas developed in one field will give insight to scientists from the other and that a common language and methodology will develop.
authors.affiliation: Centre for Non-linear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada.
authors.forename: Peter S
authors.initials: PS
authors.lastname: Swain
authors.forename: André
authors.initials: A
authors.lastname: Longtin
mesh_terms: D000818:Animals; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors