Cellular noise is random variability in quantities arising in cellular biology. For example, cells which are genetically identical, even within the same tissue, are often observed to have different expression levels of proteins, different sizes and structures. [1] [2] These apparently random differences can have important biological and medical consequences. [3]
Cellular noise was originally, and is still often, examined in the context of gene expression levels – either the concentration or copy number of the products of genes within and between cells. As gene expression levels are responsible for many fundamental properties in cellular biology, including cells' physical appearance, behaviour in response to stimuli, and ability to process information and control internal processes, the presence of noise in gene expression has profound implications for many processes in cellular biology.
The most frequent quantitative definition of noise is the coefficient of variation:[ citation needed ]
where is the noise in a quantity , is the mean value of and is the standard deviation of . 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:
and the normalized variance:
The first experimental account and analysis of gene expression noise in prokaryotes is from Becskei & Serrano [4] and from Alexander van Oudenaarden's lab. [5] The first experimental account and analysis of gene expression noise in eukaryotes is from James J. Collins's lab. [6]
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. [7]
An issue with the general depiction of extrinsic noise as a spread along the main diagonal in dual-reporter studies is the assumption that extrinsic factors cause positive expression correlations between the two reporters. In fact, when the two reporters compete for binding of a low-copy regulator, the two reporters become anomalously anticorrelated, and the spread is perpendicular to the main diagonal. In fact, any deviation of the dual-reporter scatter plot from circular symmetry indicates extrinsic noise. Information theory offers a way to avoid this anomaly. [8]
Note: These lists are illustrative, not exhaustive, and identification of noise sources is an active and expanding area of research.
Note that extrinsic noise can affect levels and types of intrinsic noise: [19] for example, extrinsic differences in the mitochondrial content of cells lead, through differences in ATP levels, to some cells transcribing faster than others, affecting the rates of gene expression and the magnitude of intrinsic noise across the population. [17]
Note: These lists are illustrative, not exhaustive, and identification of noise effects is an active and expanding area of research.
As many quantities of cell biological interest are present in discrete copy number within the cell (single DNAs, dozens of mRNAs, hundreds of proteins), tools from discrete stochastic mathematics are often used to analyse and model cellular noise. [31] [32] In particular, master equation treatments – where the probabilities of observing a system in a state at time are linked through ODEs – have proved particularly fruitful. A canonical model for noise gene expression, where the processes of DNA activation, transcription and translation are all represented as Poisson processes with given rates, gives a master equation which may be solved exactly (with generating functions) under various assumptions or approximated with stochastic tools like Van Kampen's system size expansion.
Numerically, the Gillespie algorithm or stochastic simulation algorithm is often used to create realisations of stochastic cellular processes, from which statistics can be calculated.
The problem of inferring the values of parameters in stochastic models (parametric inference) for biological processes, which are typically characterised by sparse and noisy experimental data, is an active field of research, with methods including Bayesian MCMC and approximate Bayesian computation proving adaptable and robust. [33] Regarding the two-state model, a moment-based method was described for parameters inference from mRNAs distributions. [30]
In a dynamical system, bistability means the system has two stable equilibrium states. A bistable structure can be resting in either of two states. An example of a mechanical device which is bistable is a light switch. The switch lever is designed to rest in the "on" or "off" position, but not between the two. Bistable behavior can occur in mechanical linkages, electronic circuits, nonlinear optical systems, chemical reactions, and physiological and biological systems.
Cellular differentiation is the process in which a stem cell changes from one type to a differentiated one. Usually, the cell changes to a more specialized type. Differentiation happens multiple times during the development of a multicellular organism as it changes from a simple zygote to a complex system of tissues and cell types. Differentiation continues in adulthood as adult stem cells divide and create fully differentiated daughter cells during tissue repair and during normal cell turnover. Some differentiation occurs in response to antigen exposure. Differentiation dramatically changes a cell's size, shape, membrane potential, metabolic activity, and responsiveness to signals. These changes are largely due to highly controlled modifications in gene expression and are the study of epigenetics. With a few exceptions, cellular differentiation almost never involves a change in the DNA sequence itself. Metabolic composition, however, gets dramatically altered where stem cells are characterized by abundant metabolites with highly unsaturated structures whose levels decrease upon differentiation. Thus, different cells can have very different physical characteristics despite having the same genome.
Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce end products, proteins or non-coding RNA, and ultimately affect a phenotype. These products are often proteins, but in non-protein-coding genes such as transfer RNA (tRNA) and small nuclear RNA (snRNA), the product is a functional non-coding RNA. The process of gene expression is used by all known life—eukaryotes, prokaryotes, and utilized by viruses—to generate the macromolecular machinery for life.
A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).
A circadian clock, or circadian oscillator, also known as one’s internal alarm clock is a biochemical oscillator that cycles with a stable phase and is synchronized with solar time.
Upstream stimulatory factor 1 is a protein that in humans is encoded by the USF1 gene.
Intrinsic immunity refers to a set of cellular-based anti-viral defense mechanisms, notably genetically encoded proteins which specifically target eukaryotic retroviruses. Unlike adaptive and innate immunity effectors, intrinsic immune proteins are usually expressed at a constant level, allowing a viral infection to be halted quickly. Intrinsic antiviral immunity refers to a form of innate immunity that directly restricts viral replication and assembly, thereby rendering a cell non-permissive to a specific class or species of viruses. Intrinsic immunity is conferred by restriction factors preexisting in certain cell types, although these factors can be further induced by virus infection. Intrinsic viral restriction factors recognize specific viral components, but unlike other pattern recognition receptors that inhibit viral infection indirectly by inducing interferons and other antiviral molecules, intrinsic antiviral factors block viral replication immediately and directly.
Developmental noise or stochastic noise is a concept within developmental biology in which the observable characteristics or traits (phenotype) varies between individuals even though both individuals share the same genetic code (genotypes) and the other environmental factors are completely the same. Factors that influence the effect include stochastic, or randomized, gene expression and other cellular noise.
Alexander van Oudenaarden is a Dutch biophysicist and systems biologist. He is a leading researcher in stem cell biology, specialising in single cell techniques. In 2012 he started as director of the Hubrecht Institute and was awarded three times an ERC Advanced Grant, in 2012, 2017, and 2022. He was awarded the Spinoza Prize in 2017.
Transcriptional bursting, also known as transcriptional pulsing, is a fundamental property of genes in which transcription from DNA to RNA can occur in "bursts" or "pulses", which has been observed in diverse organisms, from bacteria to mammals.
Transcriptional noise is a primary cause of the variability (noise) in gene expression occurring between cells in isogenic populations. A proposed source of transcriptional noise is transcriptional bursting although other sources of heterogeneity, such as unequal separation of cell contents at mitosis are also likely to contribute considerably. Bursting transcription, as opposed to simple probabilistic models of transcription, reflects multiple states of gene activity, with fluctuations between states separated by irregular intervals, generating uneven protein expression between cells. Noise in gene expression can have tremendous consequences on cell behaviour, and must be mitigated or integrated. In certain contexts, such as establishment of viral latency, the survival of microbes in rapidly changing stressful environments, or several types of scattered differentiation, the variability may be essential. Variability also impacts upon the effectiveness of clinical treatment, with resistance of bacteria and yeast to antibiotics demonstrably caused by non-genetic differences. Variability in gene expression may also contribute to resistance of sub-populations of cancer cells to chemotherapy and appears to be a barrier to curing HIV.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
Johan Paulsson is a Swedish mathematician and systems biologist at Harvard Medical School. He is a leading researcher in systems biology and stochastic processes, specializing in stochasticity in gene networks and plasmid reproduction.
Synthetic biological circuits are an application of synthetic biology where biological parts inside a cell are designed to perform logical functions mimicking those observed in electronic circuits. Typically, these circuits are categorized as either genetic circuits, RNA circuits, or protein circuits, depending on the types of biomolecule that interact to create the circuit's behavior. The applications of all three types of circuit range from simply inducing production to adding a measurable element, like green fluorescent protein, to an existing natural biological circuit, to implementing completely new systems of many parts.
Within bioinformatics, intrinsic Noise Analyzer (iNA) is an open source software for studying reaction kinetics in living cells. The software analyzes mathematical models of intracellular reaction kinetics such as gene expression, regulatory networks or signaling pathways to quantify concentration fluctuations due to the random nature of chemical reactions.
Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.
In cell biology, single-cell variability occurs when individual cells in an otherwise similar population differ in shape, size, position in the cell cycle, or molecular-level characteristics. Such differences can be detected using modern single-cell analysis techniques. Investigation of variability within a population of cells contributes to understanding of developmental and pathological processes,
State switching is a fundamental physiological process in which a cell/organism undergoes spontaneous, and potentially reversible, transitions between different phenotypes. Thus, the ability to switch states/phenotypes is a key feature of development and normal function of cells within most multicellular organisms that enables the cell to respond to various intrinsic and extrinsic cues and stimuli in a concerted fashion enabling them to ‘make’ appropriate cellular decisions. Although state switching is essential for normal functioning, the repertoire of phenotypes in a normal cell is limited.
Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the RNA concentration of hundreds to thousands of genes. Single-cell transcriptomics makes it possible to unravel heterogeneous cell populations, reconstruct cellular developmental pathways, and model transcriptional dynamics — all previously masked in bulk RNA sequencing.
Monoallelic gene expression (MAE) is the phenomenon of the gene expression, when only one of the two gene copies (alleles) is actively expressed (transcribed), while the other is silent. Diploid organisms bear two homologous copies of each chromosome (one from each parent), a gene can be expressed from both chromosomes (biallelic expression) or from only one (monoallelic expression). MAE can be Random monoallelic expression (RME) or Constitutive monoallelic expression (constitutive). Constitutive monoallelic expression occurs from the same specific allele throughout the whole organism or tissue, as a result of genomic imprinting. RME is a broader class of monoallelic expression, which is defined by random allelic choice in somatic cells, so that different cells of the multi-cellular organism express different alleles.
