Division in Escherichia coliis triggered by a sizesensing rather than a timing mechanism
 Lydia Robert†^{1, 2}Email author,
 Marc Hoffmann^{3},
 Nathalie Krell^{4},
 Stéphane Aymerich^{1, 2},
 Jérôme Robert^{5} and
 Marie Doumic†^{6, 7}
DOI: 10.1186/174170071217
© Robert et al.; licensee BioMed Central Ltd. 2014
Received: 31 December 2013
Accepted: 24 February 2014
Published: 28 February 2014
Abstract
Background
Many organisms coordinate cell growth and division through size control mechanisms: cells must reach a critical size to trigger a cell cycle event. Bacterial division is often assumed to be controlled in this way, but experimental evidence to support this assumption is still lacking. Theoretical arguments show that size control is required to maintain size homeostasis in the case of exponential growth of individual cells. Nevertheless, if the growth law deviates slightly from exponential for very small cells, homeostasis can be maintained with a simple ‘timer’ triggering division. Therefore, deciding whether division control in bacteria relies on a ‘timer’ or ‘sizer’ mechanism requires quantitative comparisons between models and data.
Results
The timer and sizer hypotheses find a natural expression in models based on partial differential equations. Here we test these models with recent data on singlecell growth of Escherichia coli. We demonstrate that a sizeindependent timer mechanism for division control, though theoretically possible, is quantitatively incompatible with the data and extremely sensitive to slight variations in the growth law. In contrast, a sizer model is robust and fits the data well. In addition, we tested the effect of variability in individual growth rates and noise in septum positioning and found that size control is robust to this phenotypic noise.
Conclusions
Confrontations between cell cycle models and data usually suffer from a lack of highquality data and suitable statistical estimation techniques. Here we overcome these limitations by using high precision measurements of tens of thousands of single bacterial cells combined with recent statistical inference methods to estimate the division rate within the models. We therefore provide the first precise quantitative assessment of different cell cycle models.
Keywords
Cell cycle Bacteria Division Size control Structured population equations Numerical simulations Nonparametric estimationBackground
Coordination between cell growth and division is often carried out by ‘size control’ mechanisms, where the cell size has to reach a certain threshold to trigger some event of the cell cycle, such as DNA replication or cell division [1]. As an example, the fission yeast Schizosaccharomyces pombe exhibits a size threshold at mitosis [2, 3]. The budding yeast Saccharomyces cerevisiae also uses a size control mechanism that acts at the G1S transition [4, 5]. In contrast, in some cells such as those of early frog embryos, progression in the cell cycle is size independent and relies on a ‘timer’ mechanism [6].
Bacterial division is often assumed to be under size control but conclusive experimental evidence is still lacking and the wealth of accumulated data presents a complex picture. In 1968, building on the seminal work of Schaechter et al. and Helmstetter and Cooper, Donachie suggested that initiation of DNA replication is triggered when the bacterium reaches a critical size [7–9]. This provided the basis for a longstanding model of size control where cell size triggers replication initiation, which in turn determines the timing of division (see [10] and references therein). However, the coupling of replication initiation to cell mass has been repeatedly challenged [11–13]. In particular, on the basis of recent singlecell analysis, the team headed by N Kleckner proposed that replication initiation is more tightly linked to the time elapsed since birth than to cell mass [13, 14]. In addition, the extent to which initiation timing affects division timing is unclear. In particular, variations in initiation timing are known to lead to compensatory changes in the duration of chromosome replication (see [15–17] and references therein). These studies argue against a size control model based on replication initiation. Another model postulates that size control acts directly on septum formation [18, 19]. Nevertheless, the nature of the signals triggering the formation of the septal ring and its subsequent constriction are still unknown [17, 20] and no molecular mechanism is known to sense cell size and transmit the information to the division machinery in bacteria.
Besides the work of Donachie, the assumption of size control in bacteria originates from a theoretical argument stating that such a control is necessary in exponentially growing cells to ensure cell size homeostasis, i.e. to maintain a constant size distribution through successive cycles. The growth of bacterial populations has long been mathematically described using partial differential equation (PDE) models. These models rely on hypotheses on division control: the division rate of a cell, i.e. the instantaneous probability of its dividing, can be assumed to depend either on cell age (i.e. the time elapsed since birth) or cell size. In the classical ‘sizer’ model, the division rate depends on size and not on age whereas in the ‘timer’ model it depends on age and not on size. Mathematical analysis of these models sheds light on the role of size control in cell size homeostasis. In particular, it has been suggested that for exponentially growing cells, a timer mechanism cannot ensure a stable size distribution [21, 22]. Nevertheless, this unrealistic behavior of the timer mechanism is based on a biologically meaningless assumption, namely the exponential growth of cells of infinitely small or large size [23, 24]. Cells of size zero or infinity do not exist and particularly small or large cells are likely to exhibit abnormal growth behavior. In conclusion, the mathematical arguments that were previously developed are insufficient to rule out a sizeindependent, timer model of bacterial division: quantitative comparisons between models and data are needed.
In the present study, we test whether age (i.e. the time elapsed since birth) or size is a determinant of cell division in E. coli. To do so, we analyzed two datasets derived from two major singlecell experimental studies on E. coli growth, performed by Stewart et al.[25] and Wang et al.[26]. Our analysis is based on division rate estimation by stateoftheart nonparametric inference methods that we recently developed [27, 28]. The two datasets correspond to different experimental setups and image analysis methods but lead to similar conclusions. We show that even though a model with a simple timer triggering division is sufficient to maintain cell size homeostasis, such a model is not compatible with the data. In addition, our analysis of the timer model shows that this model is very sensitive to hypotheses regarding the growth law of rare cells of very small or large size. This lack of robustness argues against a timer mechanism for division control in E. coli as well as in other exponentially growing organisms. In contrast, a model where cell size determines the probability of division is in good agreement with experimental data. Unlike the timer model, this sizer model is robust to slight modifications of the growth law of individual cells. In addition, our analysis reveals that the sizer model is very robust to phenotypic variability in individual growth rates or noise in septum positioning.
Results and discussion
Description of the data
Age and size distribution of the bacterial population
The results reported in this study were obtained from the analysis of two different datasets, obtained through microscopic timelapse imaging of single E. coli cells growing in a rich medium, by Stewart et al. [25] and Wang et al.[26]. Stewart et al. followed single E. coli cells growing into microcolonies on LBagarose pads at 30°C. The length of each cell in the microcolony was measured every 2 min. Wang et al. grew cells in LB: Luria Bertani medium at 37°C in a microfluidic setup [26] and the length of the cells was measured every minute. Due to the microfluidic device structure, at each division only one daughter cell could be followed (data s_{ i }: sparse tree), in contrast to the experiment of Stewart et al. where all the individuals of a genealogical tree were followed (data f_{ i }: full tree). It is worth noting that the different structures of the data f_{ i } and s_{ i } lead to different PDE models, and the statistical analysis was adapted to each situation (see below and Additional file 1). From each dataset (f_{ i } and s_{ i }) we extracted the results of three experiments (experiments f_{1},f_{2} and f_{3} and s_{1},s_{2} and s_{3}). Each experiment f_{ i } corresponds to the growth of approximately six microcolonies of up to approximately 600 cells and each experiment s_{ i } to the growth of bacteria in 100 microchannels for approximately 40 generations.
Testing the timer versus sizer models of division
Agestructured (timer) and sizestructured (sizer) models
In this model, a cell of age a at time t has the probability B_{a}(a)d t of dividing between time t and t+d t.
In the Size Model, a cell of size x at time t has the probability B_{s}(x)d t of dividing between time t and t+d t. This model is related to the socalled sloppy size control model [33] describing division in S. pombe.
For simplicity, we focused here on a population evolving along a full genealogical tree, accounting for f_{ i } data. For data s_{ i } observed along a single line of descendants, an appropriate modification is made to Equations (1) and (2) (see Additional file 1: Supplementary Text).
Testing the Age Model (timer) and the Size Model (sizer) with experimental data
In this augmented setting, the Age Model governed by the PDE (1) and the Size Model governed by (2) are restrictions to the hypotheses of an agedependent or sizedependent division rate, respectively (B_{a,s}=B_{a} or B_{a,s}=B_{s}).
The density n(t,a,x) of cells having age a and size x at a large time t can be approximated as n(t,a,x)≈e^{ λ t }N(a,x), where the coefficient λ>0 is called the Malthus coefficient and N(a,x) is the stable agesize distribution. This regime is rapidly reached and time can then be eliminated from Equations (1), (2) and (3), which are thus transformed into equations governing the stable distribution N(a,x). Importantly, in the timer model (i.e. B_{a,s}=B_{a}), the existence of this stable distribution requires that growth is subexponential around zero and infinity [23, 24].
We estimate the division rate B_{a} of the Age Model using the age measurements of every cell at every time step. Likewise, we estimate the division rate B_{s} of the Size Model using the size measurements of every cell at every time step. Our estimation procedure is based on mathematical methods we recently developed. Importantly, our estimation procedure does not impose any particular restrictions on the form of the division rate function B, so that any biologically realistic function can be estimated (see Additional file 1: Section 4 and Figure S6). In Additional file 1: Figures S1 and S2, we show the sizedependent and agedependent division rates B_{s}(x) and B_{a}(a) estimated from the experimental data. Once the division rate has been estimated, the stable age and size distribution N(a,x) can be reconstructed through simulation of the Age & Size Model (using the experimentally measured growth rate; for details see the Methods).
We measure the goodnessoffit of a model (timer or sizer) by estimating the distance between two distributions: the agesize distribution obtained through simulations of the model with the estimated division rate (as explained above), and the experimental agesize distribution. Therefore, a small distance indicates a good fit of the model to the experimental data. To estimate this distance we use a classical metric, which measures the average of the squared difference between the two distributions. As an example, the distance between two bivariate Gaussian distributions with the same mean and a standard deviation difference of 10% is 17%, and a 25% difference in standard deviation leads to a 50% distance between the distributions. The experimental agesize distribution is estimated from the age and size measurements of every cell at every time step of a given experiment f_{ i } or s_{ i }, thanks to a simple kernel density estimation method.
Analysis of singlecell growth
The agesize joint distribution of E. colicorresponds to a sizedependent division rate
As an additional analysis to strengthen our conclusion, we calculated the correlation between the age at division and the size at birth using the experimental data. If division is triggered by a timer mechanism, these two variables should not be correlated, whereas we found a significant correlation of −0.5 both for s_{ i } and f_{ i } data (P<10^{−16}; see Additional file 1: Figure S7).
We used various growth functions for x<x_{ m i n } and x>x_{ m a x } but a satisfying fit could not be obtained with the Age Model. In addition, we found that the results of the Age Model are very sensitive to the assumptions made for the growth law of rare cells of very small and large size (see Additional file 1: Figure S3). This ultrasensitivity to hypotheses regarding rare cells makes the timer model unrealistic generally for any exponentially growing organisms.
In contrast, the Size Model is in good agreement with the data (Figure 3: A compared to E and B compared to F) and allows a satisfactory reconstruction of the agesize structure of the population. The shape of the experimental and fitted distributions as well as their localization along the yaxis and xaxis are similar (size distributions and age distributions, i.e. projections onto the yaxis and xaxis, are shown in Additional file 1: Figure S8).
The quantitative measure of goodnessoffit defined above is coherent with the curves’ visual aspects: for the Size Model the distance between the model and the data ranges from 17% to 20% for f_{ i } data (16% to 26% for s_{ i } data) whereas for the Age Model it ranges from 51% to 93% for f_{ i } data (45% to 125% for s_{ i }).
The experimental data has a limited precision. In particular, the division time is difficult to determine precisely by image analysis and the resolution is limited by the time step of image acquisition (for s_{ i } and f_{ i } data, the time step represents respectively 5% and 8% of the average division time). By performing stochastic simulations of the Size Model (detailed in Additional file 1: Section 6), we evaluated the effect of measurement noise on the goodness of fit of the Size Model. We found that noise of 10% in the determination of the division time leads to a distance around 14%, which is of the order of the value obtained with our experimental data. We conclude that the Size Model fits the experimental data well. Moreover, we found that in contrast to the Age Model, the Size Model is robust with respect to the mathematical assumptions for the growth law for small and large sizes: the distance changes by less than 5%.
Size control is robust to phenotypic noise
Noise in the biochemical processes underlying growth and division, such as that created by stochastic gene expression, may perturb the control of size and affect the distribution of cell size. We therefore investigated the robustness of size control to such phenotypic noise. The Size Model describes the growth of a population of cells with variable age and size at division. Nevertheless, it does not take into account potential variability in individual growth rate or the difference in size at birth between two sister cells, i.e. the variability in septum positioning. To do so, we derived two PDE models, which are revised Size Models with either growth rate or septum positioning variability (see Additional file 1: Supplementary Text) and ran these models with different levels of variability.
Variability in individual growth rate has a negligible effect on the size distribution
For each single cell, a growth rate can be defined as the rate of the exponential increase of cell length with time [25, 26]. By doing so, we obtained the distribution of the growth rate for the bacterial population (Additional file 1: Figure S4A). In our dataset this distribution is statistically compatible with a Gaussian distribution with a coefficient of variation of approximately 8% (standard deviation/mean =0.08).
Variability in septum positioning has a negligible effect on size distribution
The cells divide into two daughter cells of almost identical length. Nevertheless, a slight asymmetry can arise as an effect of noise during septum positioning. We found a 4% variation in the position of the septum (Additional file 1: Figure S4B), which is in agreement with previous measurements [35, 37–39]. To test the robustness of size control to noise in septum positioning, we extended the Size Model to allow for different sizes of the two sister cells at birth (the equation is given in Additional file 1: Section 5). We ran this model using the empirical variability in septum positioning (shown in Additional file 1: Figure S4B) and compared the resulting size distribution to the one obtained by simulations without variability. As shown in Figure 4B (comparing the red and blue lines), the effect of natural noise in septum positioning is negligible. We also ran the model with higher levels of noise in septum positioning and found that a three times higher (12%) coefficient of variation is necessary to obtain a 10% change in size distribution (Figure 4B inset, and Additional file 1: Figure S5).
Conclusions
In the present study, we present statistical evidence to support the hypothesis that a sizedependent division rate can be used to reconstruct the experimental agesize distribution of E. coli. In contrast, this distribution cannot be generated by a timer model where the division rate depends solely on age. Even though the timer model can maintain cell size homeostasis, it is quantitatively incompatible with the observed size distribution. Our analysis of two different datasets shows the robustness of our conclusions to changes in experimental setup and image analysis methods. Our results therefore confirm the hypothesis of size control of division in E. coli. In addition, our analysis of the timer model shows that it is very sensitive to mathematical assumptions for the growth law of very rare cells of abnormal size, suggesting that this model is unrealistic for any exponentially growing organisms.
Noise in biochemical processes, in particular gene expression, can have a significant effect on the precision of biological circuits. In particular, it can generate a substantial variability in the cell cycle [5]. Therefore we investigated in bacteria the robustness of size control to noise in the singlecell growth rate and septum positioning, using appropriate extensions of the Size Model. We found that variability of the order of what we estimated from E. coli data does not significantly perturb the distribution of cell size. Therefore, in a natural population exhibiting phenotypic noise, the control of cell size is robust to fluctuations in septum positioning and individual growth rates. From a modeling perspective, this demonstrates that the simple Size Model is appropriate for describing a natural bacterial population showing phenotypic diversity.
Our approach is based on comparisons between PDE models and singlecell data for the cell cycle. Such comparisons were attempted a few decades ago using data from yeasts (e.g. [21, 33]). Nevertheless, these interesting studies were hampered by the scarcity and poor quality of singlecell data as well as the lack of appropriate statistical procedures to estimate the division rate within the models. In contrast, we used highprecision measurements of tens of thousands cells in combination with modern statistical inference methods, which allowed us to assess quantitatively the adequacy of different models. We think this approach could prove successful in studying other aspects of the cell cycle, such as the coordination between replication and division or the molecular mechanisms underlying size control of division. Several different mechanisms involved in division control in bacteria have already been unraveled, in particular MinCD inhibition and nucleoid occlusion [40–42]. We believe that a better understanding of the relative roles played by MinCD inhibition and nucleoid occlusion in division control can be gained by analyzing the agesize distributions of minCD and nucleoid occlusion mutants. We are therefore currently performing timelapse microscopy experiments to record the growth of such mutants.
Methods
Data analysis
The data of Stewart et al. contain the results of several experiments performed on different days, each of them recording the simultaneous growth of several microcolonies of the MG1655 E. coli strain on LBagar pads at 30°C, with a generation time of approximately 26 min [25]. The first 150 min of growth were discarded to limit the effects of nonsteadystate growth (cells undergo a slight plating stress when put on microscopy slides and it takes several generations to recover a stable growth rate). For the dataset obtained by Wang et al., the MG1655 E. coli strain was grown in LB at 37°C in a microfluidic device with a doubling time of approximately 20 min. To avoid any effect of replicative aging such as described in [26], we only kept the first 50 generations of growth. In addition the first ten generations were discarded to ensure steadystate growth. Both datasets were generated by analyzing fluorescent images (the bacteria express the Yellow Fluorescent Protein) using two different software systems. For s_{ i } data, cell segmentation was based on the localization of brightness minima along the channel direction (see [26]). In the same spirit, for f_{ i } data, local minima of fluorescence intensity were used to outline the cells, following by an erosion and dilation step to separate adjacent cells (see [25]). To measure its length, a cell was approximated by a rectangle with the same second moments of pixel intensity and location distribution (for curved cells the measurement was done manually).
For both datasets we extracted data from three experiments done on different days. We did not pool the data together to avoid statistical biases arising from daytoday differences in experimental conditions. Each analysis was performed in parallel on the data corresponding to each experiment.
Numerical simulations and estimation procedures
meeting the CFL: CourantFriedrichsLewy stability criterion. We simulated n(t,a,x) iteratively until the agesize distribution reached stability ((n(t+d t,a,x)−n(t,a,x))<10^{−8}). To eliminate the Malthusian parameter, the solution n(t,a,x) was renormalized at each time step (for details see [43]).
The agedependent division rate B_{a} for each experiment was estimated for the cell age grid [0,A_{ m a x }] using Equation (14) and (16) of Additional file 1. Using this estimated division rate, the agesize distributions corresponding to the Age Model (Figure 3C,D) were produced by running the Age & Size Model. As explained in the main text, we used various growth functions for small and large cells (i.e. for x<x_{ m i n } and x>x_{ m a x }; between x_{ m i n } and x_{ m a x } growth is exponential with the same rate as for the Size Model). For instance for the fit of the experiment f_{1} shown in Figure 3C, for x<2.3 µm and x>5.3 µm, v(x)= max(p(x),0), with p(x)=−0.0033x^{3}+0.036x^{2}−0.094x+0.13. Likewise, for the fit of the experiment s_{1} shown in Figure 3D, for x<3.5 µm and x>7.2 µm, v(x)= max(p(x),0), with p(x)=−0.0036x^{3}+0.063x^{2}−0.33x+0.67. For each dataset the polynomial p(x) was chosen as an interpolation of the function giving the length increase as a function of length (shown in Figure 2B for f_{1} data).
Simulations of the extended size models with variability in growth rates or septum positioning (Equations (23) and (24) in Additional file 1) were performed as for the Age & Size Model, with an upwind finite volume scheme. To simulate Equation (23), we used a grid composed of 2^{7} equally spaced points on [ 0,X_{ m a x }] and 100 equally spaced points on [ 0.9v_{ m i n },1.1v_{ m a x }], where v_{ m i n } and v_{ m a x } are the minimal and maximal individual growth rates in the data.
Notes
Abbreviations
 PDE:

partial differential equation.
Declarations
Acknowledgments
We thank S Jun and E Stewart for sharing their data, D Chatenay, E Stewart, J Hoffmann, M Elez and G Batt for critical reading of the manuscript and Richard James for English editing. The research of M Doumic was supported by the ERC Starting Grant SKIPPER ^{ A D }.
Authors’ Affiliations
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