(C) PLOS One [1]. This unaltered content originally appeared in journals.plosone.org.
Licensed under Creative Commons Attribution (CC BY) license.
url:https://journals.plos.org/plosone/s/licenses-and-copyright

------------



Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

['Arantxa Urchueguía', 'Biozentrum', 'University Of Basel', 'Basel', 'Swiss Institute Of Bioinformatics', 'Luca Galbusera', 'Dany Chauvin', 'Gwendoline Bellement', 'Thomas Julou', 'Erik Van Nimwegen']

Date: 2022-01 

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

As TFs change their expression levels across growth conditions, so will the fluctuations in their binding at their target promoters. Consequently, a key characteristic that distinguishes noise propagation from other sources of expression noise is that this noise will be highly condition-dependent. Therefore, a systematic investigation of how genome-wide noise levels of promoters vary across condition should directly provide insights into the role of noise propagation.

That noise propagation may play an important role for genome-wide gene expression noise was suggested by results we obtained in a previous study in which we measured genome-wide gene expression noise of E. coli promoters in a single growth condition and compared this with expression noise of synthetic promoters that were selected from a large library of 100 to 150 bp random sequence fragments [ 9 ]. We not only found that the synthetic promoters generally exhibited low expression noise, but also found that native promoters with high expression noise tended to have more known regulatory inputs from TFs than genes with low expression noise. To explain these observations, we developed an evolutionary theory in [ 9 ] explaining why natural selection may favor noisy gene regulation in many situations. However, to what extent genome-wide gene expression noise is indeed determined by noise propagation is currently unclear, and one of the motivations of this study is to systematically investigate this experimentally.

Alternatively, instead of a promoter’s noise level being an intrinsic feature of its architecture, a promoter’s noise might be determined by the way it is regulated in a given condition. Since the transcription rate of a promoter will generally depend on the binding of transcription factors (TFs), a promoter’s transcription rate will fluctuate as TFs stochastically bind and unbind to it. The rates of binding and unbinding of TFs in turn depend on average expression levels and fluctuations in expression levels of TFs across cells [ 8 , 12 – 14 ]. Consequently, fluctuations in both the expression levels of TFs and their binding to promoter regions will thus unavoidably propagate to fluctuations in expression of their target genes [ 15 – 19 ].

A systematic investigation into the condition dependence of genome-wide gene expression noise may provide important insights into what drives both absolute and relative noise levels of promoters. For example, it is possible that transcriptional noise is mostly driven by fluctuations in general factors, for example, the concentrations of RNA polymerases and nucleotides, and the overall state of the DNA. For example, it has been suggested that noise levels in yeast are mainly determined by basic promoter architecture and associated nucleosome positioning (see [ 10 ] and citations therein). Similarly, since supercoiling of the DNA has been reported to control the sizes of transcriptional bursts in Escherichia coli [ 11 ], it is conceivable that a promoter’s noise properties depend on its sensitivity to supercoiling. If differences in transcriptional noise across promoters result mainly from differences in the sensitivity of promoters to such global factors, then one would expect the same promoters to show highest noise across conditions.

In addition, because genome-wide studies have so far focused on gene expression noise in a single growth condition, it is currently not clear to what extent gene expression noise in bacteria is condition-dependent. That is, we do not know to what extent absolute noise levels vary across growth conditions and whether genes with the highest noise in one condition also exhibit the highest noise in other conditions.

It is by now well established that isogenic cells growing in a homogeneous environment show cell-to-cell fluctuations in gene expression (for example, [ 1 – 4 ]). This gene expression noise is not surprising from a biophysical perspective, given the inherent thermodynamic fluctuations in the molecular events underlying gene expression and the small numbers of molecules involved. In the simplest models of gene expression, where promoters are transcribed at a constant rate, the “intrinsic” noise in gene expression would simply grow in proportion to the square root of a gene’s absolute expression level (for example, [ 5 ]). However, even in bacteria where the gene expression process is considerably simpler than in eukaryotes, genes typically exhibit significantly higher levels of transcriptional noise, indicating that transcription rates fluctuate in time and across cells due to “extrinsic” factors [ 1 ]. Moreover, studies of genome-wide gene expression noise in bacteria have shown that genes with the same absolute expression can exhibit different noise levels and that the transcriptional noise of a gene is to a substantial extent encoded in its promoter sequence [ 6 – 9 ]. However, how the promoter sequence of a gene determines its transcriptional noise and what factors are the main drivers of differences in transcriptional noise remains largely unknown.

Results

Individual promoters show highly diverse changes in noise across conditions If changes in noise levels across conditions were mostly driven by fluctuations in global factors such as concentrations of RNA polymerase, we would expect different genes to exhibit coherent changes in noise across conditions. For example, relative noise levels of different genes may remain relatively unchanged across conditions, or alternatively, noise levels might rescale across conditions as a function of the mean expression of the gene in the condition. However, this is not what we observe. Instead, different promoters show highly diverse changes in their noise levels across conditions (Fig 2). PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 2. Individual promoters show diverse patterns of variation in noise levels across conditions. (A) Scatter plot showing the expression plasticity (variance across conditions, horizontal axis) and noise (variance in noise across conditions) of all measured promoters. (B-G) Examples of condition-dependent mean and noise of individual promoters. Each panel shows the noise level as a function of mean across conditions (colors; see legend) for one promoter, with the gene regulated by the promoter indicated in each panel. Error bars denote standard errors of the estimates based on biological replicate measurements. Each of the 3 pairs of panels indicate different types of behavior in mean and noise across conditions, as described at the top of each pair of panels. The underlying data for Fig 2 can be found in S1 Data and https://doi.org/10.5281/zenodo.4662163. https://doi.org/10.1371/journal.pbio.3001491.g002 Following the general usage of the word plasticity to refer to the adaptability of the phenotype to changes in the environment, we will refer to the variance of a promoter’s mean and noise level across conditions as the plasticity of its mean and noise. The plasticity of both mean and noise vary over a substantial range across promoters, without any clear systematic dependence between these quantities. Analogous scatter plots for the variation and dependence between average expression, average noise, and the plasticities in mean and noise show that all these quantities vary substantially across promoters (Fig K in S1 Text). That is, individual promoters show highly distinct variation in their mean and noise across conditions, and Fig 2B–2G shows some examples of the different behaviors we observe. Note that all observations in these panels have error bars that show the standard error of measured mean and noise across biological replicates. We observe promoters that are low noise in almost all conditions, either with high plasticity in mean (Fig 2B) or low plasticity in mean (Fig 2C). Other promoters show high noise with plasticity in both the mean and noise level, without clear correlation between mean and noise level (Fig 2D and 2E). But many other patterns of behavior can be observed, such as promoters that show only low noise when the promoter has high mean (Fig 2F) or only low noise when the promoter has low mean (Fig 2G). The growth media were not predictive for how individual genes were going to change their mean and noise. For example, while overall the whole library is shifted towards lower noise in synthetic rich media, individual genes can show higher noise in this condition compared to other conditions (for example, Fig 2B and 2F). We highlighted this particular condition as an example, but the same observation applies to others. These observations indicate that global changes in the cell physiology or in the expression level cannot explain how the noise of a promoter varies across conditions. This implies that there is a promoter-specific source of noise shaping condition-dependent gene expression variability. Just as the plasticity in mean expression derives from gene regulation, one obvious hypothesis is that this promoter-dependent source of condition-dependent noise derives from gene regulation as well.

Noise propagation predicts that relative noise levels are condition-dependent As mentioned in the introduction, the mechanistic basis for gene expression regulation is that the binding and unbinding of TFs to a promoter causes the transcription rate from this promoter to change. Consequently, fluctuations in the expression levels of TFs and their binding to promoter regions will unavoidably propagate to fluctuations in the expression of their target genes [8,12–19]. While the general decrease of absolute noise levels with growth rate (Fig 1C and 1E) is likely due to general physiological fluctuations that affect all promoters, the highly diverse changes in the relative noise levels of different promoters across conditions (Fig 2) is exactly what is expected to occur under a noise propagation scenario (Fig 3). PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 3. Signatures of condition-dependent noise propagation. (A) We imagine a scenario in which 2 promoters are each regulated by a single transcription factor (TF1 or TF2). In growth condition 1, TF2 shows a higher variability in its activity (orange distribution) than TF1 (blue distribution). As a result, its target (gene B, yellow) will show higher expression variability than the target of TF1 (gene A, pink). (B) If the relative levels of variability in the activities are reversed in a different condition, the relative noise levels of target genes A and B will likewise be reversed. That is, noise propagation can explain why transcriptional noise is highly condition-dependent. (C) Because the noise of a target gene depends on fluctuations in activities of all of the TFs that regulate it, promoters that are more regulated will typically show higher noise levels in all conditions. The illustration shows a promoter controlling the expression of gene C (green) which is regulated both by TF1 (blue) and TF2 (orange). Since at least one of these TFs is highly variable in each condition, gene C will exhibit high noise levels in both conditions. https://doi.org/10.1371/journal.pbio.3001491.g003 Let us consider a simple scenario in which 2 individual genes are each regulated by one TF, that is, gene A is regulated by TF1 and gene B by TF2 (Fig 3A). As the activities of these TFs fluctuate within a given condition, these fluctuations can propagate to their respective targets. For example, in a condition where TF1 exhibits less variation in activity from cell to cell than TF2, gene A will generally exhibit less expression noise than gene B (Fig 3A). In anticipation of analysis presented below, it is important to stress that the distribution of “TF activity” shown in Fig 3A is only a schematic representation of a much more complicated biophysical process at the molecular level, and different target promoters of the same TF might respond very differently to fluctuations in the TF’s “activity.” Roughly speaking, the extent to which a TF X will propagate noise to a given target promoter Y depends on how much the binding of TF X to promoter Y fluctuates in time and across cells and how much the transcription rate of promoter Y depends on these fluctuations in binding of TF X. For example, if promoter Y is already strongly repressed or activated by another TF, the binding of TF X may be irrelevant for its transcription, and TF X will not propagate noise to promoter Y. Even if the transcription rate of Y is sensitive to binding of TF X, it may still be that binding affinities of the sites in promoter Y are so weak that the promoter is essentially never bound or so strong that it is essentially always bound, even if the concentration of TF X fluctuates from cell to cell. Only those target promoters of X for which the transcription rate is both sensitive to the binding of TF X, and for which the binding of TF X fluctuates significantly, will experience significant increase in their noise levels. Thus, the amount of noise propagation from a given TF X to a given target promoter Y is a complex context-dependent function, and only a subset of the promoters that are targeted by TF X will indeed respond to fluctuations in the activity of TF X in a given condition. These considerations make clear that, in general, we expect the extent to which different TFs propagate noise to different target promoters to be highly condition-dependent. For example, for the simple scenario imagined in Fig 3A, we can easily imagine that, in another condition, TF1 may show higher variability than TF2, such that the noise levels of their targets would change accordingly (Fig 3B). In other words, if gene expression noise is to a large extent determined by noise propagation from regulators to their targets, then this would explain why relative noise levels of genes can vary in a complex manner across conditions, because we expect both the noise levels of different regulators and the sensitivity to this noise at different promoters to vary across conditions. In summary, we propose that the qualitative patterns in expression noise across conditions that we observed in Fig 2 and Fig K in S1 Text can be explained by assuming that noise levels are to a large extent determined by propagation of noise from regulators to their targets. The hypothesis that noise propagation is responsible for the observed condition-dependent relative noise levels makes a number of additional predictions. First, constitutive promoters, that is, promoters that are not targeted by any TF other than the sigma factor of the RNA polymerase, should exhibit low noise in each condition and relatively little plasticity in their noise levels. Second, the larger the number of regulators that target a given promoter, the larger the chance that the promoter will be sensitive to fluctuations in the activities of at least one of these TFs (Fig 3C). Thus, more noisy promoters are in general expected to have more regulatory inputs. In addition, because all regulatory inputs of a promoter can change their noise levels in a condition-dependent manner, we also expect that, the more regulatory inputs a promoter has, the higher the plasticity of its noise level will be. Finally, to the extent that the regulatory inputs of each promoter are known, it should be possible to explain why some promoters are more noisy in one condition, and other promoters more noisy in another condition, and identify which TFs are most responsible for noise propagation in different conditions. In the next section, we investigate whether our data indeed exhibit these properties.

Noise propagation explains the condition-dependent noise levels of genes In a previous work [9], we found that, for cells growing in minimal media with glucose, more noisy genes generally have more regulatory inputs, and we here checked whether these observations generalize to multiple growth conditions. We sorted promoters by their noise levels and used the regulatory site annotation from RegulonDB [28] to calculate the average number of known regulatory inputs of genes with noise levels N pc above a certain cutoff level, as a function of the cutoff level (Materials and methods). We find that in all 8 conditions, the number of known regulatory inputs systematically increases with noise levels (Fig 4A and Fig L in S1 Text). Notably, these differences are highly statistically significant with t-statistics of 4 or higher for the difference between known regulatory inputs for promoters above and below a given noise cutoff across a wide range of cutoffs in each condition (Fig M in S1 Text). Next, we wanted to test whether constitutive promoters exhibit consistently low noise levels. This analysis is complicated by the fact that our knowledge of E. coli’s regulatory network is extremely incomplete, with no known target promoters for almost two-thirds of E. coli’s TFs. Thus, although no known regulatory input is known for almost 60% of E. coli promoters (Fig N in S1 Text), a substantial fraction of these promoters are likely regulated by TFs for which we currently lack information. To obtain a set of promoters that are very likely constitutive we took a random selection of synthetic promoters that we obtained previously by screening a library of 100 to 150 bp random sequence fragments for sequences that drive expression in M9 minimal media with glucose [9] (see Supplementary Methods in S1 Text). We measured mean expression and expression noise of these synthetic promoters across 4 growth conditions and compared their expression plasticity, average noise, and noise plasticity with those of native promoters that have at least one known regulatory input. We found that the synthetic promoters not only have lower expression plasticity (p-value = 1.545e-09, two-sided Welch’s t test), confirming that they are likely constitutive but that both their average noise (p < 2.2e-16, two-sided Welch’s t test) and noise plasticity (p = 6.209e-05, two-sided Welch’s t test) are systematically low in comparison with regulated promoters (Fig O in S1 Text). To test whether all high noise promoters have at least one regulatory input, we calculated what fraction of promoters with noise level over a given cutoff have at least one known regulatory input (Fig 4B and Fig P in S1 Text) and found that 70% to 90% of high noise promoters in each condition have at least one known regulatory input. Given that our current knowledge of the regulatory network only represents one-third of E. coli’s TFs, this strongly suggests that most, if not all, of the high noise promoters are indeed regulated. We next tested to what extent noise plasticity increases with the amount of known regulatory inputs of a promoter. As shown in Fig 4C, we indeed observe that genes with more regulatory inputs show larger noise plasticity compared to genes with few or no known regulatory inputs (p<3.7×10−10, two-sided Welch’s t test). That is, regulated genes are not only more noisy on average, their noise levels are also more regulated across conditions. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 4. Noise propagation explains condition-dependent noise levels. (A) More noisy promoters tend to have more regulatory inputs. We sorted promoters by their average noise across the 8 conditions and calculated the mean (y-axis) and standard error (gray area) of the average number of TFs known to regulate the promoters with noise level above , as a function of (x-axis). (B) The fraction of regulated promoters increases with higher levels of noise. We sorted promoters as in panel A and calculated the fraction (y-axis) and standard error (gray area) of the number of promoters with at least 1 regulatory input with noise level above , as a function of (x-axis). (C) The noise plasticity increases with number of regulatory inputs of the promoter. Shown are the cumulative distributions of the variance in noise across the 8 conditions for promoters with no known regulatory inputs (blue), 1 or 2 known regulators (yellow), and 3 or more known regulators (red). (D) The Motif Activity Response Analysis model explains a significant fraction of the variation in noise levels. Shown is the percentage of explained variance (FOV %, y-axis) in each of the 8 conditions (x-axis) after running the model on the real dataset (gray bars) and on randomized data (orange bars). Randomized data were generated by shuffling the association between regulatory inputs and expression noise multiple times and shown is the average value obtained +/− its standard error. (E) Table of TFs predicted by the model to significantly propagate noise in a condition-specific manner, that is, with A rc >δA rc in only one condition. (F) Average noise propagation activities ( , y-axis) and their error bars ( , vertical lines) of the strongest 6 noise propagators (with ), sorted by significance ( , x-axis), which consistently propagate noise across all 8 conditions. The underlying data for Fig 4 can be found in S1 Data and https://doi.org/10.5281/zenodo.4662163. https://doi.org/10.1371/journal.pbio.3001491.g004 If noise propagation is responsible for the high condition dependence of the relative noise levels across conditions, then it should in principle be possible to explain changes in the relative noise levels of promoters in terms of their regulatory inputs, and changes in the amount of noise that different TFs are propagating in different conditions. We have previously developed a model, called Motif Activity Response Analysis [29,30], which models gene expression in terms of computationally predicted regulatory sites in promoters genome-wide using a simple linear model, to identify which TFs are most important for driving observed gene expression changes across a set of conditions. We here adapted this approach to investigate whether changes in relative noise levels of promoters across conditions can be explained in terms of changes in the “noise propagating activities” of regulators and to identify which TFs are most important for propagating noise in different conditions. In particular, we used the RegulonDB database [28] to set a binary matrix of known regulatory inputs, that is, S pr is 1 when promoter p is known to be regulated by TF r and 0 otherwise. We then model the noise N pc of each promoter p in each condition c as a simple linear function of its known regulatory inputs S pr and the unknown noise propagating activities A rc of each regulator r in each condition c: (1) where is the average noise level of all promoters in condition c, is the average of S pr across all promoters, and ϵ is a noise term that is assumed Gaussian distributed with mean 0 and unknown variance. For each condition c, we then inferred the noise propagating activities A rc by fitting the model (1) using a Gaussian prior on the activities A rc to avoid overfitting, which allows us to calculate a full posterior probability distribution over the activities A rc [30]. There are many reasons why the crude model (1) is extremely unlikely to provide a good quantitative model for the measured noise levels. First, as already mentioned above, our current knowledge of E. coli’s regulatory network is very incomplete with no targets known for almost two-thirds of its TFs, that is, there may well be significantly more regulatory interactions that we do not know about than those that we happen to know about. Second, as discussed in the previous section, the extent to which noise from a given TF propagates to a given target is likely a complex function of the combination of TFs that target a given promoter, the numbers, positions, and affinities of the binding sites for each of these TFs, the concentrations of all these TFs in a given condition, and so on. In particular, it is likely that of all promoters that a given TF targets, only a fraction will be sensitive to the noise in the TF binding in a given condition. However, we currently have no knowledge whatsoever about the extent to which different targets may respond to noise in the TFs that regulate them in a given condition. In absence of such knowledge, Eq (1) makes the crude assumption that each TF will propagate the same amount of noise to all its (known) target promoters and that the total noise of a promoter is simply the sum of the noise propagated by each of the regulators. Note that the latter effectively assumes that the fluctuations in the binding of all TFs are mutually independent, which is also unlikely to be true. Consequently, the aim of the model (1) is not to explain noise levels of individual promoters or to quantify the amount of noise propagated by each TF. Rather, the aim is to test whether this crude model of noise propagation can explain a significant fraction of the variation in noise levels across promoters and to identify which TFs are most responsible for noise propagation in each condition. As shown in Fig 4D (gray bars), in spite of our highly incomplete and rudimentary knowledge of E. coli’s regulatory network, the simple model explains between 10% and 30% of the variance in noise levels across conditions. To confirm the significance of these results, we fit the same model to data in which the association between regulatory inputs and noise levels were randomized by randomly shuffling the rows of the noise matrix N pc and observed that the fraction of explained variance on the randomized data was always much lower than on the real data (Fig 4D, orange bars). The model of Eq (1) also calculates error bars δA rc for the estimated noise propagation activities A rc of each regulator r in each condition c, allowing us to infer which TFs are most significantly propagating noise in each condition and Fig Q in S1 Text shows, for each condition, all TFs for which the noise propagating activity was larger than its error bar, that is, A rc >δA rc . Note that, while activity A rc corresponds to the average amount of additional noise per target that regulator r is predicted to cause in condition c, this should not be interpreted as the typical amount of noise per target. As discussed above, different target promoters will have very different sensitivities to the noise of regulator r, so that the A rc reflects an average between weak or no noise propagation at many targets and much stronger noise propagation at a subset of the targets of r. Focusing first on TFs that propagate noise in a highly condition-specific manner, Fig 4E lists the 5 TFs that had significant noise propagating activity in only one condition. For several of these TFs, their known functional role is consistent with the prediction that they propagate noise in these specific conditions. To mention the most obvious case, the TF LexA is predicted to propagate noise only in the sub-MIC ciprofloxacin condition. LexA is a repressor of the SOS response genes, and it is known that ciprofloxacin causes DNA damage and induces the SOS response [31]. Since we employed ciprofloxacin at a concentration well below the minimal inhibitory concentration, DNA damage likely only occurred in a subset of the cells, leading to heterogeneity in LexA activity across the cells. Similarly, the model predicted that FlhDC, the master regulator of flagellar biosynthesis [32], significantly propagates noise only in early stationary phase. It is known that flagellar synthesis peaks toward the end of exponential phase and decreases shortly after entry into stationary phase [33]. Since the 16-h condition is a transition between late exponential growth and entry into stationary phase, it seems plausible that some cells had entered growth arrest and were no longer expressing components of the flagellar machinery, while others had not yet transitioned, causing heterogeneity in the expression of targets of FlhDC. The other examples of condition-specific noise propagators are discussed in the S1 Text. In addition to condition-specific noise propagators, we noted that many of the most significant noise propagators were found in multiple conditions (Fig Q in S1 Text). To identify regulators that were consistently contributing to noise propagation in all conditions, we calculated, for each regulator r, its average noise propagating activity averaged over all conditions (SI Methods and Texts in S1 Text). Fig 4F shows the 6 TFs that were most significantly propagating noise in all conditions. As discussed in more detail in the S1 Text, the appearance of many of these TFs likely reflects our experimental setup, that is, growth in minimal media in microtiter plates. For example, the early stationary phase and stress regulator Sigma38 (rpoS) has been shown to have heterogeneous activity across single cells in M9 media with glucose [34]. Similarly, limiting oxygen levels in microtiter plates can lead to production of fermentation products [35,36], which are known to acidify the medium [37], explaining the appearance of GadW and GadX, which are involved in the response to acid stress [38]. The prediction that the histone-like TF H.NS is the most significant noise propagating TF is interesting, given that in eukaryotes, noise properties of different genes have been related to nucleosome organization in their promoters [39]. Although the predicted condition-dependent role of these TFs in propagating noise are, at this point, just hypotheses that require in-depth experimental follow-up to confirm, for several cases, the predicted role in noise propagation by these TFs is highly plausible, given their known functional role, and highlights that the simple model can make concrete predictions about which TFs are most involved in driving gene expression noise in different conditions. In summary, we have presented multiple lines of evidence to confirm that noise propagation plays an important role in determining condition-dependent expression noise genome-wide. Constitutive promoters have consistently low noise and low noise plasticity across conditions. In contrast, across all conditions, we find that the higher the expression noise, the higher the number of known regulatory inputs promoters tend to have. Although almost 60% of promoters have no known regulatory input, 70% to 90% of high noise promoters have at least one known regulatory input. In addition, promoters with more known regulatory inputs also exhibit higher noise plasticity across conditions, indicating that gene regulation causes noise levels to be regulated as well. And finally, in spite of our very limited knowledge of E. coli’s regulatory network, a crude model of noise propagation explains 10% to 30% of the variance in relative noise levels across conditions. Together, these results imply that propagation of noise through the regulatory network is a major determinant of condition-dependent expression noise. That is, not only the mean expression levels of genes are determined by gene regulation, the noise levels of genes are to a substantial extent determined by the structure of the gene regulatory network as well.

[END]

[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001491

(C) Plos One. "Accelerating the publication of peer-reviewed science."
Licensed under Creative Commons Attribution (CC BY 4.0)
URL: https://creativecommons.org/licenses/by/4.0/


via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/