Cellular noise

The most frequent quantitative definition of noise is the coefficient of variation: : \eta_X = \frac{\sigma_X}{\mu_X}, where \eta_X is the noise in a quantity X, \mu_X is the mean value of X and \sigma_X is the standard deviation of X. This measure is dimensionless, allowing a relative comparison of the importance of noise, without necessitating knowledge of the absolute mean. Other quantities often used for mathematical convenience are the Fano factor: : F_X = \frac{\sigma_X^2}{\mu_X}. and the normalized variance: : N_X = \eta_X^2 = \frac{\sigma_X^2}{\mu_X^2}. == Experimental measurement ==

The first experimental account and analysis of gene expression noise in prokaryotes is from Becskei & Serrano and from Alexander van Oudenaarden's lab. The first experimental account and analysis of gene expression noise in eukaryotes is from James J. Collins's lab. == Intrinsic and extrinsic noise ==

Cellular noise is often investigated in the framework of intrinsic and extrinsic noise. Intrinsic noise refers to variation in identically regulated quantities within a single cell: for example, the intra-cell variation in expression levels of two identically controlled genes. Extrinsic noise refers to variation in identically regulated quantities between different cells: for example, the cell-to-cell variation in expression of a given gene. Intrinsic and extrinsic noise levels are often compared in dual reporter studies, in which the expression levels of two identically regulated genes (often fluorescent reporters like GFP and YFP) are plotted for each cell in a population. == Sources ==

Note: These lists are illustrative, not exhaustive, and identification of noise effects is an active and expanding area of research. • Gene expression levels: noise in gene expression causes differences in the fundamental properties of cells, limits their ability to biochemically control cellular dynamics, • Drug resistance: Noise improves short-term survival and long-term evolution of drug resistance at high levels of drug treatment. Noise has the opposite effect at low levels of drug treatment; • Cancer treatments: recent work has found extrinsic differences, linked to gene expression levels, in the response of cancer cells to anti-cancer treatments, potentially linking the phenomenon of fractional killing (whereby each treatment kills some but not all of a tumour) to noise in gene expression. Because individual cells could repeatedly and stochastically perform transitions between states associated with differences in responsiveness to a therapeutic modality (chemotherapy, targeted agent, radiation, etc.), therapy might need to be administered frequently (to ensure cells are treated soon after entering a therapy-responsive state, before they can rejoin the therapy-resistant subpopulation and proliferate) and over long times (to treat even those cells emerging late from the final residue of the therapy-resistant subpopulation). • Evolution of genome: Genome are covered by chromatin that can be roughly classified into "open" (also known as euchromatin) or "closed" (also known as heterochromatin). Open chromatin leads to less noise in transcription compared to heterochromatin. Often "housekeeping" proteins (which are proteins that carry out tasks in the required for cellular survival) work large multiprotein complexes. If the noise in proteins of such complexes are to discoordinated, it can lead to reduced level of production of multiprotein complexes, with potentially deleterious effects. Reduction in noise may provide an evolutionary selection movement of essential genes into open chromatin. • Information processing: as cellular regulation is performed with components that are themselves subject to noise, the ability of cells to process information and perform control is fundamentally limited by intrinsic noise == Analysis ==

File:Modelling stochastic gene expression.svg|thumb|400px|A canonical model for stochastic gene expression, known as the two-state or telegraph Regarding the two-state model, a moment-based method was described for parameters inference from mRNAs distributions. == References ==