 Research
 Open Access
 Published:
Efficient computation of stochastic cellsize transient dynamics
BMC Bioinformatics volume 20, Article number: 647 (2019)
Abstract
Background
How small, fastgrowing bacteria ensure tight cellsize distributions remains elusive. Highthroughput measurement techniques have propelled efforts to build modeling tools that help to shed light on the relationships between cell size, growth and cycle progression. Most proposed models describe cell division as a discrete map between size at birth and size at division with stochastic fluctuations assumed. However, such models underestimate the role of cell size transient dynamics by excluding them.
Results
We propose an efficient approach for estimation of cell size transient dynamics. Our technique approximates the transient size distribution and statistical moment dynamics of exponential growing cells following an adder strategy with arbitrary precision.
Conclusions
We approximate, up to arbitrary precision, the distribution of division times and size across time for the adder strategy in rodshaped bacteria cells. Our approach is able to compute statistical moments like mean size and its variance from such distributions efficiently, showing close match with numerical simulations. Additionally, we observed that these distributions have periodic properties. Our approach further might shed light on the mechanisms behind gene product homeostasis.
Introduction
Stochastic modeling of bacterial cell division has been widely used in systems biology[1–4]. Basic problems concerning the stochastic nature of cell biology include modeling of cell size distributions[5], effects of fluctuations in division control in terms of population fitness[6] and autocorrelation and spectral analysis of division strategies through several generations[7]. The importance of a stochastic outlook of the cell division control has been highlighted in literature considering physiological implications that potentially affect DNA concentration, surface transport and biosynthesis rates, as well as proteome composition[8].
Stochastic models can achieve high level of detail. Nowadays, predictions of stochastic modeling have been challenged experimentally by increasingly accurate highthroughput measurements of cellular variables enabled by timelapse imaging, image processing and microfluidic devices for fine environmental control. These experiments have elucidated division strategies in rod shaped microorganisms like bacteria[2, 3], yeast[9] and archea[10].
Stochastic models for bacterial division control aim to explain how bacteria decide when to split into two descendants. These models can be divided in two main groups: Discrete stochastic maps (DSM) and Continuous Rate Models (CRM)[11]. DSM, the most used, are based on the idea that at a phenomenological, coarsegrained level, a size regulation strategy can be studied using the properties of division events. Hence, the division strategy is a map that takes cell size at birth s_{b} to a targeted cell size at division s_{d} trough a deterministic function s_{d}=f(s_{b}) plus stochastic fluctuations that have to be assumed[1, 7].
Depending on the mapping s_{d}=f(s_{b}), or traditionally between the added size Δ=s_{d}−s_{b} and s_{b}, division strategies are classified into three main paradigms: one is the timer strategy, in which a cell waits for a fixed time, on average, and then divides (Δ decreases with s_{b}). Another is the sizer, in which a cell grows until it reaches a certain volume[12]before dividing (Δ increases with s_{b}). The third one is the adder, a recently observed division strategy [2, 13], in which the cell grows adding, on average, a fixed size since the last division event (Δ does not depend on s_{b}).
In contrast to the simple description given by a DSM approach, continuous rate models (CRMs) explain not only these mapping but other interesting phenomena. CRM consider, besides discrete division events, the cell cycle dynamics. This class of models describes the division as a continoustime stochastic process with an associated division rate h (also known as splitting rate function) that sets the probability of division into an infinitesimal time interval. Currently, the main problem with CRM is that it is not obvious a priori how to parametrize the division rate h given experimental setups [11].
Here, we propose an efficient approach for the analysis and estimation of the division of rodshaped organisms based on CRMs. We will show how CRMs allow us to reproduce observed correlations between key cellsize variables for the adder strategy, as well as time dynamics of the cell size distribution, which are unavailable for traditional DSMs.
Our splitting rate function (h) is assumed proportional to the current cellsize. With this h, we build a continuous time Markov chain (CTMC) which transient dynamics can be estimated numerically using the finite state projection (FSP)[14] approach. FSP maps the infinite set of the states \(n\in \mathbb {N}\) of a Markov chain onto a set with a finite number of states (for example n∈{0,1,2,3,4}). The transient probability distribution of such finite state Markov chain can approximated by using standard numerical ODE solvers.
Methods
CRM of bacteria cellsize transient dynamics
Consider a bacterial cell growing exponentially in size (s(t)) as
where μ is the cell growth rate with individual cellsize doubling time τ= ln2/μ. s_{0} is the initial size of the cell. Let the cell divide at time t_{1}; then the size after division (assuming no partitioning errors) is given by
After n(t) divisions, the size can be written as
Hence, the cell size dynamics can be rewritten as the dynamics of the counting process n(t). Let the rate of the counting process n(t) be
As we show in Additional file 1, using this rate, we conclude that the size at division in a cell cycle given the newborn size s_{b} is an exponential random variable with probability distribution
where Δ=s_{d}−s_{b} is the the added size, and \(\overline {\Delta }=\frac {\mu }{k}\). By this result we get:
which corresponds to an adder DSM model with average added size \(\bar {\Delta }\). Next, we present the transient dynamics of the size distribution that can be obtained using this CRM. Further details describing this CRM have been published in past studies[15].
Results
Cellsize transient distribution for the adder strategy
Let P_{i}(t) represent the probability of the counting process n(t) being in the state n(t)=i (cell divided i times at time t) and the transition rate h=ks with s given by (3). Then, the master equation that describes the dynamics of P_{i}(t) is given by
where δ_{i,j} is the Kronecker delta. The solution for P_{i}(t) knowing P_{i−1}(t) is given by
where
Analytic expressions for the first five P_{i}(t) are shown in Additional file 1, this distribution \(\vec {P}\) can be efficiently obtained, either analytic or numerical, through the solution of the truncated set of ODEs defined in (8). An numeric solution in addition to (9) can be obtained using finite state projection[14] and computing the matrix exponential associated to the master equation(8). This approach is shown in Additional file 1.
Once solved (9), we obtained time trends for some P_{i}(t) which are plotted in Fig. 1.
Using this P_{i}s, the transient dynamics of the mean number of divisions \(\langle n\rangle =\sum n P_{n}(t)\) and their variance \(\text {var}(n)=\sum _{n} (n\langle n\rangle)^{2}P_{n}(t)\) can be calculated. These dynamics are in perfect agreement with the results based on stochastic simulation algorithms (SSA) as can be seen in Fig. 2. After a few divisions, the distribution \(\overrightarrow {P_{i}}\) reaches a mean \(\langle n\rangle \rightarrow \frac {t}{\tau }\) and the variance reaches a finite limit when t→∞ around 0.75 (no exact expression was calculated).
As we show in Additional file 1, in the limit of t→∞ the distribution of P_{i} satisfies
suggesting an asymptotic invariance under translation on, simultaneously, n→n+1 and t→t+τ. This invariance is also satisfied by the size \(s(t)=\frac {s_{0} e^{\mu t}}{2^{n(t)}}\). This property will be used to obtain the limit cellsize distribution in the following section.
Size distribution of independent cells
Consider a set of independent cells, all of them growing exponentially at rate μ. We assume that once one cell divides, we only keep one of the descendant cells, the other descendant is discarded. Hence, the size population is fixed at all times. Experimentally, this is usually obtained in microfluidbased experiments like the mother machine[2, 16].
For simplicity, let us assume that all cells started at t=0 with size s_{0}, i.e. with initial distribution
Our goal is to compute the distribution of cell sizes over the population at time t>0.
Using (12) and (9), the probability distribution of cell sizes after a time (t) of a population of independent cells is given by
Distribution (13) corresponds to a sum of weighted Dirac delta distributions δ(x) with positions centered on sizes (3). The mean and variance of the size are given by
Figure 3 shows moment dynamics (14) projected over the ten first states (P_{i}) on the time interval (0,7τ). Theoretical and SSA simulations over 10K cells are compared.
As consequence of the periodic conditions (11), the size distribution (13) is the same after a division time τ. Equivalently, for a fixed t, the position of the Deltas will change depending on the initial size s_{0}. Figure 4 shows how this effect arises. Note how the deltas draw an enveloping curve changing s_{0} or equivalently advancing on time. Deltas of cells starting from different starting sizes (from s_{0} to 2s_{0}) measured at time t=7τ are shown. These deltas are compared with data computed using SSA showing excellent agreement.
This envelope distribution could be important in future estimations of cell distributions in actual experiments.
Discussion
Some details here are worth being discussed. First, as was pointed out previously[17], the proposed splitting rate function reproduces the adder DSM, this is, the observed decorrelation between the added size (Δ=s_{d}−s_{b}) and the size at birth. This behavior was found by most experimental studies[2, 16]. However, the noise in added size taken as the \(CV_{\Delta }^{2}\) seems to be higher than the one experimentally observed (while our typical \(CV^{2}_{\Delta }\) is 1, experimentally it is as small as 0.1). This low noise can be reached considering a multistep process as suggested by [17], although this would make our model more complex. We will elaborate on this idea in upcoming studies.
The idea behind this control mechanism relies on the definition of a splitting rate function dependent of the size. As pointed out by some authors [2, 13], the splitting could correspond to the formation of the FtsZ ring. Here, our assumption would be that the formation of this ring has a rate proportional to the size of the bacteria. The dependence on size has been suggested by previous observations [18, 19].
Although the assumption that all cells start at a fixed size seems quite unrealistic, extensions to cases where the initial cell size correspond to a distribution can be easily done. Note that such distribution should be convoluted with the distribution obtained using our proposed approach. Some effects of a starting size distribution with finite variance are shown in additional file 1.
Extrapolation of this approach to division strategies away from the adder strategy is not too difficult. As we have shown in [15], we can get other strategies by considering a SRF that is nonlinearly dependent on the size; i.e. h=ks^{λ}. Further discussion is implemented in Additional file 1 and the full description of this approach will be done in upcoming publications.
Biological implications of this approach are extensive. Transient dynamics of cell size might unveil details on the mechanisms behind gene product homeostasis [8, 20]. Additionally, this dynamics might provide tools for quantifying the noise transmitted by the stochasticity of division events. The relationship between SRF functions and cell size control strategies further enable the use of recently proposed frameworks for gene expression[21] and cell lineage[22] analysis of experimental data from proliferating cell populations.
Conclusions
Continuous rate models (CRM) for division control of rodshaped bacteria are uncommon due to scarce mappings to experimental results. Here, starting from a splitting rate function proportional to the size, we explore its implication on the division control. We compute the expected number of divisions during a given time interval and its variance, and the dynamics of the size distribution of a population of independent cells.
Size dynamics of rodshaped organisms can be described by a continoustime Markov chain. This model describes the division as a singlestep process with occurrence rate proportional to the cell size. In past studies, we showed how this rate yields to an adder strategy which is, usually, taken as the main paradigm of cell division. Here, we explore the transient dynamics of cell size distribution considering this division strategy. Numeric estimations were done using the finite state projection algorithm.
We consider cells starting at same conditions and see how size statistics evolves. We perform some preliminary predictions like the distribution of division times and the size distribution along the time showing the evolution of mean size and its variance. We also observe that these distributions have periodic properties with an associated period of one division time.
Availability of data and materials
Not applicable.
Abbreviations
 CRM:

Continuous rate model
 CTMC:

Continuoustime Markov chain
 DSM:

Discrete stochastic model
 FSP:

Finite state projection
 SRF:

Splittingrate function
References
 1
Amir A. Cell size regulation in bacteria. Phys Rev Lett. 2014; 112(20):208102.
 2
TaheriAraghi S, Bradde S, Sauls JT, Hill NS, Levin PA, Paulsson J, Vergassola M, Jun S. Cellsize control and homeostasis in bacteria. Curr Biol. 2015; 25(3):385–91.
 3
IyerBiswas S, Wright CS, Henry JT, Lo K, Burov S, Lin Y, Crooks GE, Crosson S, Dinner AR, Scherer NF. Scaling laws governing stochastic growth and division of single bacterial cells. Proc Nat Acad Sci. 2014; 111(45):15912–7.
 4
Modi S, VargasGarcia CA, Ghusinga KR, Singh A. Analysis of noise mechanisms in cellsize control. Biophys J. 2017; 112(11):2408–18.
 5
Koch A. Bacterial Growth and Form: Springer; 2001.
 6
Hashimoto M, Nozoe T, Nakaoka H, Okura R, Akiyoshi S, Kaneko K, Kussell E, Wakamoto Y. Noisedriven growth rate gain in clonal cellular populations. Proc Nat Acad Sci. 2016; 113(12):3251–6.
 7
Tanouchi Y, Pai A, Park H, Huang S, Stamatov R, Buchler NE, You L. A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature. 2015; 523(7560):357.
 8
Willis L, Huang KC. Sizing up the bacterial cell cycle. Nature Rev Microbiol. 2017; 15(10):606.
 9
Nobs JB, Maerkl SJ. Longterm single cell analysis of s. pombe on a microfluidic microchemostat array. PloS one. 2014; 9(4):93466.
 10
Eun YJ, Ho PY, Kim M, LaRussa S, Robert L, Renner LD, Schmid A, Garner E, Amir A. Archaeal cells share common size control with bacteria despite noisier growth and division. Nature Microbiol. 2018; 3(2):148.
 11
Ho PY, Lin J, Amir A. Modeling cell size regulation: From singlecelllevel statistics to molecular mechanisms and populationlevel effects. Ann Rev Biophys. 2018; 47:251–71.
 12
Facchetti G, Chang F, Howard M. Controlling cell size through sizer mechanisms. Curr Opin Syst Biol. 2017; 5:86–92.
 13
Si F, Le Treut G, Sauls JT, Vadia S, Levin PA, Jun S. Mechanistic origin of cellsize control and homeostasis in bacteria. Curr Biol. 2019; 29(11):1760–70.
 14
Munsky B, Khammash M. The finite state projection approach for the analysis of stochastic noise in gene networks. IEEE Trans Autom Contr. 2008; 53(Special Issue):201–14.
 15
NietoAcuna CA, AriasCastro JC, SanchezIsaza CA, VargasGarcia CA, Pedraza JM. Characterization of cell division control strategies through continuous rate models. arXiv preprint arXiv:1905.13377. 2019.
 16
Campos M, Surovtsev IV, Kato S, Paintdakhi A, Beltran B, Ebmeier SE, JacobsWagner C. A constant size extension drives bacterial cell size homeostasis. Cell. 2014; 159(6):1433–46.
 17
VargasGarcía CA, Singh A. Elucidating cell size control mechanisms with stochastic hybrid systems. In: 2018 IEEE Conference on Decision and Control (CDC). IEEE: 2018. p. 4366–71.
 18
Jasani A, Huynh T, Kellogg D. Growthdependent activation of protein kinases links cell cycle progression to cell growth. BioRxiv. 2019:610469.
 19
Patterson JO, Rees P, Nurse P. Noisy cellsizecorrelated expression of cyclin b drives probabilistic cellsize homeostasis in fission yeast. Curr Biol. 2019; 29:1379–86.
 20
VargasGarcia CA, Ghusinga KR, Singh A. Cell size control and gene expression homeostasis in singlecells. Curr Opin Syst Biol. 2018; 8:109–16.
 21
Jakub Jedrak J, Kwiatkowski M, OchabMarcinek A. Exactly solvable model of gene expression in a proliferating bacterial cell population with stochastic protein bursts and protein partitioning. Phys Rev E. 2019; 99:042416.
 22
GarcíaGarcía R, Genthon A, Lacoste D. Linking lineage and population observables in biological branching processes. Phys Rev E. 2019; 99:042413.
Acknowledgements
CN thanks COLCIENCIAS convocatoria para doctorados nacionales 647 for financial support. AS is supported by the National Institute of Health Grant 1R01GM126557
About this supplement
This article has been published as part of BMC Bioinformatics Volume 20 Supplement 23, 2019: Proceedings of the Joint International GIW & ABACBS2019 Conference: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume20supplement23.
Funding
Publication costs are funded by Fundación Universitaria Konrad Lorenz.
Author information
Affiliations
Contributions
CN and CV wrote the paper. CN performed the simulations. CV analyzed the simulation results. AS and JP designed the study. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Cesar Augusto VargasGarcia.
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
NietoAcuna, C., VargasGarcia, C., Singh, A. et al. Efficient computation of stochastic cellsize transient dynamics. BMC Bioinformatics 20, 647 (2019). https://doi.org/10.1186/s1285901932137
Received:
Accepted:
Published:
Keywords
 Finite state projection
 Stochastic hybrid systems