A hopefully brief definition of biological noise

A hopefully brief definition of biological noise

Some years ago I took a course in Systems Biology (or, better, in mathematical modeling of biological systems). One day, the teacher introduced the notion of "biological noise", which was directly linked to the - imho, obvious - concept that every cellular system behaves in a fundamentally stochastic way.

Many of my biologist colleagues, however, were a little confused by how a real cell, which indeed has a very precise organization and purpose, could be working based on stochastic molecular processes. As I searched the internet for a concise and precise definition of this apparently-odd "biological noise" idea, I also found that it has been widely cited in biological literature, but apparently never really explained from scratch.

Therefore, in my master degree thesis (which, perhaps unfortunately for me, dealt with mathematical modeling of biological systems :D), I decided to spend a couple of words on the topic. Reading it after some time, I reckon that my explanation was a little naive and maybe "shallow" for an expert in the field. Anyway, I decided to repost it here, in the (vain?) hope that it could be useful for someone just approaching to the idea that biological systems are indeed not as deterministic as they might seem :).

What is biological noise?

If cellular systems functioned in a deterministic way, each cell from a genetically identical population, exposed to an identical environment, should display an identical phenotype and an identical quantity of every cellular component. However, the intracellular concentration of biomolecules can notably vary from cell to cell, even in the absence of any genetic mutations [1, 2, 3, 4]. This observation has led to the de?nition of the so-called biological (or cellular) noise, which is the variation in the quantity of a particular cellular component (generally, a protein) observed in the different members of a clonal population of cells.

The main cause of biological noise is the fact that the production and degradation of cellular components are stochastic processes, which depend on random collisions among molecules. Due to the small molecular concentrations reached inside a cell, the randomness of these collisions becomes highly in?uential on the outcome of these reactions. Note that the word stochastic does not mean that a cellular system behaves in a totally random way; it just means that it is impossible to determine with absolute certainty how the system will evolve from a certain initial state. Even if some events can be �more probable� than others, depending on the physico-chemical properties of the involved species, the global state of the system will always present a certain degree of unpredictability. E.g., a gene with a strong-affinity promoter will have a stronger probability to be expressed than a low-affinity one, but no cell in a clonal population will ever start transcribing a strong-affinity gene at the exact same time and/or rate of the rest of the population.

In particular, remember that the majority of chromosomal genes are present in a single copy per cell. Therefore, the products of their transcription and translation (RNAs and proteins) will be strongly subjected to biological noise, and their quantities will often present random ?uctuations. An example of the macroscopic effect of these fluctuations is shown is Figure 1.

Figure 1: In vivo consequences of biological noise. The ?gure shows a clonal population of Escherichia coli cells (strain RP22) expressing a single copy of the cyan (cfp) and yellow (yfp) alleles on the Green Fluorescent Protein gene, controlled by strong inducible promoters. (a) shows the effects of low cfp and yfp expression. Each cell displays a different ?uorescence color, caused by the presence of varying quantities of both Cfp and Yfp. (b) shows the effect of the full induction of the two promoters. In this case, the effect of the ?uctuations of Cfp and Yfp is averaged out by the higher quantities of proteins reached, and each cell displays the same ?uorescence color. Image taken from: [1].

Depending on its sources, two principal typologies of biological noise have been defined [1]:

• Intrinsic noise: biological noise which is directly correlated to the expression of a single gene. The cause of this noise is the fact that every transcription and translation event is triggered by stochastic collisions between the components of the transcription and translation machinery and each gene. Therefore, the same gene will never (or we better say almost surely never, based on probability laws :) ) be expressed at the exact same time in two different cells;

• �Extrinsic noise: biological noise which exerts a homogeneous effect on the expression of all the genes of a single cell, but whose value changes among different cells. This type of noise is caused by the fact that the number of molecules of the cellular factors needed for gene expression (e.g., RNA polymerases, ribosomes or transcriptional regulators) are, as every other gene of a cell, subjected to intrinsic noise. Therefore, the concentration of these factors will be different in each member of the clonal population, but it will equally impact the timing and probability of expression of all the other genes from their same cell.

As a side note, even though the notion that cellular processes are completely based on stochastic processes might seem a little nonsensical to a newbie to the field, it has been observed that stochasticity in gene expression plays an essential role in the functioning of cellular systems [5]. For instance, some cells exploit the effect of random protein ?uctuations to develop different phenotypes from the same genotype (as in the case of bistable systems). This non-genetic differentiation of a clonal population of cells helps them to adapt more quickly to a varying environment, without the need of any DNA mutations.

Bibliography

[1] Elowitz, M. B., Levine, A. J. et al., 2002. Stochastic gene expression in a single cell. Science, 297(5584):1183�6.

[2] Kaern, M., Elston, T. C. et al., 2005. Stochasticity in gene expression: from theories to phenotypes. Nature Reviews. Genetics, 6(6):451�64.

[3] Klipp, E., Herwig, R. et al., 2005. Systems Biology in practice: concepts, implementation and application. Wiley-VCH Verlag GmbH & Co. KGaA.

[4] Szallasi, Z., 2006. System modeling in cellular biology. The MIT Press.
[5] Kitano, H., 2004. Biological robustness. Nature Reviews. Genetics, 5(11):826�37.