(175f) Generalizing Noise Decomposition to Elucidate Nonlinear Gene Expression Noise

The genetic program responsible for the maintenance and operation of all living cells is executed by the collection of complex gene regulatory networks arising from genes interacting with other genes and biomolecules within a cell. Incredibly, these naturally occurring networks robustly operate even as noisy genetic information is transduced. The various biochemical sources that contribute to a gene’s expression noise have been broadly categorized as either being intrinsic or extrinsic. The categorization of gene expression noise as either being intrinsic or extrinsic is intimately defined in the context of a two-reporter synthetic system. The standard (symmetric) two-reporter design includes two, independently expressed genes each that produce measurable fluorescent proteins within a single cell. As both genes operate stochastically, their observed protein products exhibit variability. From a gene-centric perspective, the collection of stochastic sources that jointly contribute to the variability of both genes are said to be extrinsic, whereas the sources of noise that independently affect both genes are said to be intrinsic. Herein, we present a generalized theoretical framework capable of isolating the impact specific biochemical processes have on a gene’s intrinsic and extrinsic expression noise. We validate our nonlinear decomposition method using mathematical models with parameterized noise. We emphasize that our theoretical and experimental design is generalizable to any two-reporter system.