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• W2617234315 abstract "•The limiting-pool mechanism of size control successfully assembles one structure of a well defined size•Multiple structures assembled from a common limiting pool exhibit large size fluctuations•Assembly of multiple structures exhibits three characteristic timescales How the size of micrometer-scale cellular structures such as the mitotic spindle, cytoskeletal filaments, the nucleus, the nucleolus, and other non-membrane bound organelles is controlled despite a constant turnover of their constituent parts is a central problem in biology. Experiments have implicated the limiting-pool mechanism: structures grow by stochastic addition of molecular subunits from a finite pool until the rates of subunit addition and removal are balanced, producing a structure of well-defined size. Here, we consider these dynamics when multiple filamentous structures are assembled stochastically from a shared pool of subunits. Using analytical calculations and computer simulations, we show that robust size control can be achieved only when a single filament is assembled. When multiple filaments compete for monomers, filament lengths exhibit large fluctuations. These results extend to three-dimensional structures and reveal the physical limitations of the limiting-pool mechanism of size control when multiple organelles are assembled from a shared pool of subunits. How the size of micrometer-scale cellular structures such as the mitotic spindle, cytoskeletal filaments, the nucleus, the nucleolus, and other non-membrane bound organelles is controlled despite a constant turnover of their constituent parts is a central problem in biology. Experiments have implicated the limiting-pool mechanism: structures grow by stochastic addition of molecular subunits from a finite pool until the rates of subunit addition and removal are balanced, producing a structure of well-defined size. Here, we consider these dynamics when multiple filamentous structures are assembled stochastically from a shared pool of subunits. Using analytical calculations and computer simulations, we show that robust size control can be achieved only when a single filament is assembled. When multiple filaments compete for monomers, filament lengths exhibit large fluctuations. These results extend to three-dimensional structures and reveal the physical limitations of the limiting-pool mechanism of size control when multiple organelles are assembled from a shared pool of subunits. Cells consist of organelles and other large structures whose size is often matched to the size of the cell. A classic example of this is the scaling of the size of the mitotic spindle with the size of the cell in a developing embryo (Wilson, 1925Wilson E.B. The Cell in Development and Heredity. Macmillan, 1925Google Scholar). How organelles and other micrometer-sized structures within the cell are assembled and maintained to have a specific size is still not well understood. A simple idea, which seems to provide the answer in several cases, is that the cell maintains a limiting pool of a diffusible molecular component that is required for assembling the structure. In such a case, size control is simply achieved by the structure growing until the limiting pool is depleted to the point when the rates of assembly and disassembly of the structure are matched. The idea that a limiting pool plays a major role in size control was the subject of a recent review that summarized the experimental evidence for this mechanism in the assembly of diverse structures such as centrosomes, flagella, and the nucleus (Goehring and Hyman, 2012Goehring N.W. Hyman A.A. Organelle growth control through limiting pools of cytoplasmic components.Curr. Biol. 2012; 22: R330-R339Abstract Full Text Full Text PDF PubMed Scopus (135) Google Scholar). In addition, a recent in vitro study used a microfluidic system to encapsulate cytoplasm from Xenopus egg extracts in small droplets and showed that spindle size is proportional to the droplet volume, thereby suggesting that the amount of cytoplasmic material controls the size (Good et al., 2013Good M.C. Vahey M.D. Skandarajah A. Fletcher D.A. Heald R. Cytoplasmic volume modulates spindle size during embryogenesis.Science. 2013; 342: 856-860Crossref PubMed Scopus (183) Google Scholar). Another study showed inverse scaling of the size of nucleoli with nuclear size in a developing C. elegans embryo in conditions when the number of nucleoli components in the nucleoplasm was fixed, also consistent with the limiting-pool mechanism (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse size scaling of the nucleolus by a concentration-dependent phase transition.Curr. Biol. 2015; 25: 641-646Abstract Full Text Full Text PDF PubMed Scopus (177) Google Scholar). The key idea of the limiting-pool mechanism of size control is that assembly slows down as the free subunit pool is depleted and the size of the assembling structure increases. When the rate of assembly of the structure matches disassembly, the cytoplasmic (“free”) pool of the limiting component reaches the so-called critical concentration, which is equal to the dissociation constant of the assembly reaction. At this point the structure being assembled reaches a well-defined size. This is the expected assembly dynamics for a single structure, however, what happens to these dynamics when multiple structures are assembled from a shared limiting pool? In this case, once the critical concentration is established, the molecular component that is limiting could stochastically transfer from one to another structure with no change to the free concentration of this component, therefore incurring no free-energy penalty. Notably, additional size-control mechanisms can impose a free-energy penalty for such an exchange. In this paper, we study the implications of limiting-pool mechanism on the size control of multiples structures growing from a shared pool of diffusing components, when such additional size-control mechanisms are absent. Although the key ideas of our theoretical study can be extended to three-dimensional structures such as nucleoli (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse size scaling of the nucleolus by a concentration-dependent phase transition.Curr. Biol. 2015; 25: 641-646Abstract Full Text Full Text PDF PubMed Scopus (177) Google Scholar), we focus here on the filamentous structures that comprise the cytoskeleton. Filamentous structures are a particularly good model system for investigating questions relating to size control because “size” can be simply defined by the length of the filament. Most cytoskeletal structures are composed of actin filaments and microtubules, which in turn are composed of actin monomers and tubulin dimers. These subunits undergo constant turnover as they are stochastically added and removed from the structure, yet the structures themselves can be maintained at a precise size. This is important since large changes in structure size can produce significant deviations from its normal physiological functions. For example, in yeast cells intracellular transport is disrupted if actin cables overgrow and buckle (Chesarone-Cataldo et al., 2011Chesarone-Cataldo M. Guérin C. Yu J.H. Wedlich-Soldner R. Blanchoin L. Goode B.L. The myosin passenger protein Smy1 controls actin cable structure and dynamics by acting as a formin damper.Dev. Cell. 2011; 21: 217-230Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar). In addition, experiments have shown that when filamentous structures are cut to a smaller size, they often grow back to their physiological length suggesting that the length is under tight control (Marshall et al., 2005Marshall W.F. Qin H. Brenni M.R. Rosenbaum J.L. Flagellar length control system: testing a simple model based on intraflagellar transport and turnover.Mol. Biol. Cell. 2005; 16: 270-278Crossref PubMed Scopus (166) Google Scholar). In some instances, multiple filamentous structures, made from a shared pool of actin monomers or tubulin dimers, coexist within the cell’s cytoplasm. For example, actin cables and actin patches in yeast are made up of actin monomers. They have different size, shape, and function, yet they coexist in the same cytoplasm while exchanging actin monomers from an apparently common pool (Michelot and Drubin, 2011Michelot A. Drubin D.G. Building distinct actin filament networks in a common cytoplasm.Curr. Biol. 2011; 21: R560-R569Abstract Full Text Full Text PDF PubMed Scopus (120) Google Scholar). This observation raises the question, how are such diverse structures assembled and maintained from a common pool of subunits? Here, we consider the stochastic assembly of multiple filamentous structures from a common and limited pool of subunits with a specific focus on the length fluctuations of these assembled structures. We assume the simple scenario when the limiting components are the building blocks of the filamentous structures being assembled and have no other effect on the length of the filaments. From this simple, analytically tractable model of stochastic assembly we derive general conclusions about the limiting-pool mechanism, and describe its limitations in controlling the sizes of multiple structures within the cell. Notably, this approach purposefully considers the limiting monomer pool to be the only mechanism by which filament length is controlled. Cognizant of the fact that in cells multiple size-regulating mechanisms might be at play, we contend that the simple, limiting-pool mechanism discussed here is a useful “null hypothesis” against which experimental data can be analyzed (Marshall, 2016Marshall W.F. Cell geometry: how cells count and measure size.Annu. Rev. Biophys. 2016; 45: 49-64Crossref PubMed Scopus (41) Google Scholar). To the extent that the detailed quantitative predictions of the limiting-pool mechanism are not borne out by experiments (that is, the null hypothesis can be rejected based on quantitative measurements), one can be confident that other size-control mechanisms are at play. We consider the limiting-pool mechanism of size control in the context of a simple model where filaments grow from a fixed number of nucleating centers within the cell by stochastic addition of diffusing monomers. Monomers, the number of which in the cell is fixed, also stochastically dissociate from the filament. The number of filaments is fixed by the number of nucleating centers, which can be a single protein or a protein complex, and which aid in the formation of the filament. An example is provided by formins which help assemble filamentous actin structures. Formins bind to the barbed end of an actin filament and capture (profilin bound) actin monomers from solution, which are then incorporated into the growing filament (see Figure 1A). Note that the model we consider is a significant departure from textbook examples of stochastic filament assembly where every monomer in solution can serve as the site of new filament assembly. In our case filament assembly occurs only from nucleating centers. We consider three different scenarios, one when there is a single nucleating center in a cell which contains a fixed number of monomers, the case of two identical nucleating centers, and of two distinct nucleating centers, which differ in the rates at which they incorporate monomers. An example of inequivalent nucleators is provided by the two different formins Bni1 and Bnr1 in budding yeast, which assemble actin filaments at different rates (Buttery et al., 2007Buttery S.M. Yoshida S. Pellman D. Yeast formins Bni1 and Bnr1 utilize different modes of cortical interaction during the assembly of actin cables.Mol. Biol. Cell. 2007; 18: 1826-1838Crossref PubMed Scopus (93) Google Scholar). Later in this section, we turn our attention to the case of many filaments and also discuss how our results carry over to the case when three-dimensional structures are assembled from a limiting pool. First we consider the case of a single nucleating center, where a single filament is assembled by the addition and dissociation of monomers. The total number of monomers in the cell, N, is fixed, and each monomer can associate to a filament with assembly rate k+, which is proportional to the number of free monomers in the cell. Hence, for a single filament, its assembly rate starts off as k+=k+'N, but as the filament grows, it decreases to k+'(N−l), where l is the length of the filament in units of monomers. Note that the rate constant, k+', is obtained by taking the second-order rate constant for monomer addition, which has units M−1 s−1, and multiplying it by the volume of the cytoplasm within which the free monomers diffuse. The rate of assembly is thus length dependent, and assuming a constant monomer dissociation rate, k−, it leads to a peaked distribution of filament lengths (Figure 1B). To describe the dynamics of an individual filament, we model the growth and decay of the filament using the master equation formalism. The key quantity to compute is the probability, p(l, t), that the filament has a length l (measured here in units of monomers) at time t. The master equation describes the evolution of p(l, t) in time, by taking into account all the possible changes of the state (length) of filament that can occur in a small time interval Δt. The master equation for a single filament is (for l > 0)dp(l,t)dt=k+'(N−l+1)p(l−1,t)+k−p(l+1,t)−k+'(N−l)p(l,t)−k−p(l,t).(Equation 1) We compute the steady-state distribution of filament lengths by setting the left-hand side of the equation to zero and omit the time variable in p(l) to indicate the steady-state nature of the distribution. We use detailed balance, p(l)k+'(N−l)=p(l+1)k−, to obtain p(l)≈κdN−le−κd/(N−l)! (see section “Exact solutions of filament distributions” in the STAR Methods), whereκd(≡k−/k+') is a dimensionless dissociation constant for the chemical reaction of a monomer binding to filament (equal to the dissociation constant multiplied with the cell volume). For example, for actin cables in yeast cells we estimate κd ∼ 104 and N∼2 × 105, using the measured concentration of actin in yeast (∼10 μM) (Johnston et al., 2015Johnston A.B. Collins A. Goode B.L. High-speed depolymerization at actin filament ends jointly catalysed by Twinfilin and Srv2/CAP.Nat. Cell Biol. 2015; 17: 1504-1511Crossref PubMed Scopus (73) Google Scholar), the typical volume of a yeast cell (∼40 μm3) (Philips and Milo, 2015Philips R.M. Milo R. Cell Biology by the Numbers. Garland Science, 2015Google Scholar) and the measured rates of association (11.6 μM−1 s−1) and dissociation (1.4 s−1) for binding of actin monomers to actin filaments (Pollard, 1986Pollard T.D. Rate constants for the reactions of ATP- and ADP-actin with the ends of actin filaments.J. Cell Biol. 1986; 103: 2747-2754Crossref PubMed Scopus (596) Google Scholar) (see section “Estimates for actin cables in yeast” in the STAR Methods for the calculations). The mean and SD of the distribution are given by N−κd and κd, respectively. This distribution is peaked, and the same is true for the distribution of free monomers, which in fact is very close to Poisson (see the STAR Methods, “Main inferences and estimates” section). Notably, the typical length of the filament is essentially given by the number of available monomers unless k+' and k_ are fine-tuned to be close in value (see the STAR Methods, section “Fraction of monomers in filaments”). We used stochastic simulations to analyze the time evolution of the length distribution. We start with a filament of zero length growing from a single nucleating center and then follow the growth trajectory of the length of the filament in time as monomers attach and fall off. After some time, we observe the filament reaching a steady state (see Figure 1B inset), when the length distribution of the filaments no longer changes with time. The distribution extracted from these simulations matches the analytic results. The timescale over which the steady state is reached is of the order 1/k+', which can be understood as the time it takes N monomers to be taken up from the pool at a rate k+'N (see Box 1 and the STAR Methods, section “Growth timescale τg” for a more precise calculation).Box 1Experimental tests of the limiting-pool mechanismOur study makes several predictions that can be used to test the limiting-pool mechanism. In the case of a single filamentous structure assembled from a pool of monomers, the steady-state distribution of the filament length (STAR Methods, Section “Main inferences and estimates”) can be tested in experiments in which the total number of monomers is tuned. This can be achieved, for example, by using the microfluidic approach described in (Good et al., 2013Good M.C. Vahey M.D. Skandarajah A. Fletcher D.A. Heald R. Cytoplasmic volume modulates spindle size during embryogenesis.Science. 2013; 342: 856-860Crossref PubMed Scopus (183) Google Scholar). We observe that the mean length (N−κd) of the filament depends on the total number of monomers, whereas the viance (κd) does not. This result can be used as a stringent test of the limiting-pool mechanism of size control.Furthermore, if there are multiple identical structures being made from a common pool of monomers, we predict the existence of anti-correlated fluctuations of individual filament lengths over time. For the two-filament case, we predicted that these fluctuations will be observed at timescales of the order N2/k_, which can also be tuned by controlling the total number of monomers. An experiment with two inequivalent filaments assembling from a common monomer pool should also reveal the timescale of order N, during which the slower-assembling filament loses the monomers it quickly accumulated in the initial growth phase.One example where in vivo experiments can be used to test our predictions for the case of filaments is provided by fission yeast cells. These cells have two different types of actin structures, namely cables and patches which are assembled by different nucleating factors (formins and the Arp2/3 complex, respectively) (Rotty et al., 2015Rotty J.D. Wu C. Haynes E.M. Suarez C. Winkelman J.D. Johnson H.E. Haugh J.M. Kovar D.R. Bear J.E. Profilin-1 serves as a gatekeeper for actin assembly by Arp2/3-dependent and -independent pathways.Dev. Cell. 2015; 32: 54-67Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar, Suarez et al., 2015Suarez C. Carroll R.T. Burke T.A. Christensen J.R. Bestul A.J. Sees J.A. James M.L. Sirotkin V. Kovar D.R. Profilin regulates F-actin network homeostasis by favoring formin over Arp2/3 complex.Dev. Cell. 2015; 32: 43-53Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar). Recently it was shown that it is possible reduce the number of patches in yeast cells by over-expressing profilin, which is an actin-binding protein that has two specific effects on assembly: it significantly favors the formation of cables by increasing the assembly rates of formin-nucleated filaments and it inhibits Arp 2/3-mediated branching and hence represses the formation of patches (Rotty et al., 2015Rotty J.D. Wu C. Haynes E.M. Suarez C. Winkelman J.D. Johnson H.E. Haugh J.M. Kovar D.R. Bear J.E. Profilin-1 serves as a gatekeeper for actin assembly by Arp2/3-dependent and -independent pathways.Dev. Cell. 2015; 32: 54-67Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar, Suarez et al., 2015Suarez C. Carroll R.T. Burke T.A. Christensen J.R. Bestul A.J. Sees J.A. James M.L. Sirotkin V. Kovar D.R. Profilin regulates F-actin network homeostasis by favoring formin over Arp2/3 complex.Dev. Cell. 2015; 32: 43-53Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar). Thus, by regulating the level of profiling, either formin- or Arp 2/3-generated structures will take up most of the available pool of actin monomers. This observation is consistent with our calculations since we find that when two structures are competing for the same subunit pool the one that assembles faster takes up practically all subunits. Still, further experiments need to be performed in which size distributions of different structures are measured to quantitatively test predictions of the limiting monomer pool model.Predictions of the limiting-pool mechanism for the case of assembly of three-dimensional structures could be tested in C. elegans early embryo cells, where two nucleoli are assembled from a shared pool of nucleoli particles. These nucleoli grow equally in size up until cell division (∼20 min) (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse size scaling of the nucleolus by a concentration-dependent phase transition.Curr. Biol. 2015; 25: 641-646Abstract Full Text Full Text PDF PubMed Scopus (177) Google Scholar). Measurements of the nucleoli size and how they scale with the size of the nucleus are consistent with predictions of the limiting-pool mechanism (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse size scaling of the nucleolus by a concentration-dependent phase transition.Curr. Biol. 2015; 25: 641-646Abstract Full Text Full Text PDF PubMed Scopus (177) Google Scholar). Experiments have also revealed that, during the assembly phase, the size (volume) of the nucleolus grows with time to the fourth power (Berry et al., 2015Berry J. Weber S.C. Vaidya N. Haataja M. Brangwynne C.P. RNA transcription modulates phase transition-driven nuclear body assembly.Proc. Natl. Acad. Sci. U. SA. 2015; 112: E5237-E5245Crossref PubMed Scopus (287) Google Scholar). This measurement is inconsistent with the assumption of size-independent rates, k+' and k−, and also with the assumption that the rates grow in proportion to the radius of the nucleolus, presumably due to the active role played by transcription of rRNA. Regardless, in steady state we still expect the assembly and disassembly rates to be balanced, and therefore we predict the same diffusive dynamics and large fluctuations of the sizes of individual nucleoli, as long as the limiting-pool mechanism alone is responsible for their size control. Specifically, we predict that in cells engineered to have longer cell cycles one should observe the predicted large, anti-correlated fluctuations in individual nucleoli sizes.Timescales of AssemblyIn the Results section, we discussed different timescales associated with the growth of filaments from identical nucleating centers, i.e., growth and diffusion timescales and their dependence on the number of monomers in the pool. One can use those calculations to estimate timescales in the case where multiple actin cables are made from a common pool of actin monomers in the mother compartment of a budding yeast cell. Using previously published numbers for cell volume (Philips) and rates of association and dissociation of monomers to actin filaments (Pollard, 1986Pollard T.D. Rate constants for the reactions of ATP- and ADP-actin with the ends of actin filaments.J. Cell Biol. 1986; 103: 2747-2754Crossref PubMed Scopus (596) Google Scholar), we estimate k+'=5×10−3s−1. Given that the observed number of actin cables is about ten, we predict that the growth phase lasts for less than a minute, assuming that there are no additional length-control mechanisms at play. (See the STAR Methods, sections “Growth timescale τg” and “Estimates for actin cables in yeast” for details.)In contrast, we estimate the diffusion timescale to span several days (STAR Methods, Section “Diffusion timescale τd”). In other words, we should never observe order −N2 fluctuations in cable lengths on the timescale of live-cell experiments, given a division time of about 90 min. Note that, for this estimate, we assume that all the actin in the mother compartment of the budding yeast cells is used to make cables. This is a reasonable assumption as these cells have very few or no patches in their mother compartment. A substantially smaller number of actin monomers in cables could bring down the estimate of the diffusion timescale considerably due to the N2 dependence of this timescale.Of course, there could be other reasons why large length fluctuations of cables are not observed in live-cell experiments: other length-control mechanisms could be at play, which may reduce length fluctuations, and even lead the system to an altogether different steady state. Indeed, several actin- and formin-binding proteins have been shown to play an important role in controlling cable length (Chesarone-Cataldo et al., 2011Chesarone-Cataldo M. Guérin C. Yu J.H. Wedlich-Soldner R. Blanchoin L. Goode B.L. The myosin passenger protein Smy1 controls actin cable structure and dynamics by acting as a formin damper.Dev. Cell. 2011; 21: 217-230Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar, Mohapatra et al., 2015Mohapatra L. Goode B.L. Kondev J. Antenna mechanism of length control of actin cables.Plos Comput. Biol. 2015; 11: e1004160Crossref PubMed Scopus (20) Google Scholar, Mohapatra et al., 2016Mohapatra L. Goode B.L. Jelenkovic P. Phillips R. Kondev J. Design principles of length control of cytoskeletal structures.Annu. Rev. Biophys. 2016; 45: 85-116Crossref PubMed Scopus (32) Google Scholar). Our study makes several predictions that can be used to test the limiting-pool mechanism. In the case of a single filamentous structure assembled from a pool of monomers, the steady-state distribution of the filament length (STAR Methods, Section “Main inferences and estimates”) can be tested in experiments in which the total number of monomers is tuned. This can be achieved, for example, by using the microfluidic approach described in (Good et al., 2013Good M.C. Vahey M.D. Skandarajah A. Fletcher D.A. Heald R. Cytoplasmic volume modulates spindle size during embryogenesis.Science. 2013; 342: 856-860Crossref PubMed Scopus (183) Google Scholar). We observe that the mean length (N−κd) of the filament depends on the total number of monomers, whereas the viance (κd) does not. This result can be used as a stringent test of the limiting-pool mechanism of size control. Furthermore, if there are multiple identical structures being made from a common pool of monomers, we predict the existence of anti-correlated fluctuations of individual filament lengths over time. For the two-filament case, we predicted that these fluctuations will be observed at timescales of the order N2/k_, which can also be tuned by controlling the total number of monomers. An experiment with two inequivalent filaments assembling from a common monomer pool should also reveal the timescale of order N, during which the slower-assembling filament loses the monomers it quickly accumulated in the initial growth phase. One example where in vivo experiments can be used to test our predictions for the case of filaments is provided by fission yeast cells. These cells have two different types of actin structures, namely cables and patches which are assembled by different nucleating factors (formins and the Arp2/3 complex, respectively) (Rotty et al., 2015Rotty J.D. Wu C. Haynes E.M. Suarez C. Winkelman J.D. Johnson H.E. Haugh J.M. Kovar D.R. Bear J.E. Profilin-1 serves as a gatekeeper for actin assembly by Arp2/3-dependent and -independent pathways.Dev. Cell. 2015; 32: 54-67Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar, Suarez et al., 2015Suarez C. Carroll R.T. Burke T.A. Christensen J.R. Bestul A.J. Sees J.A. James M.L. Sirotkin V. Kovar D.R. Profilin regulates F-actin network homeostasis by favoring formin over Arp2/3 complex.Dev. Cell. 2015; 32: 43-53Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar). Recently it was shown that it is possible reduce the number of patches in yeast cells by over-expressing profilin, which is an actin-binding protein that has two specific effects on assembly: it significantly favors the formation of cables by increasing the assembly rates of formin-nucleated filaments and it inhibits Arp 2/3-mediated branching and hence represses the formation of patches (Rotty et al., 2015Rotty J.D. Wu C. Haynes E.M. Suarez C. Winkelman J.D. Johnson H.E. Haugh J.M. Kovar D.R. Bear J.E. Profilin-1 serves as a gatekeeper for actin assembly by Arp2/3-dependent and -independent pathways.Dev. Cell. 2015; 32: 54-67Abstract Full Text Full Text PDF PubMed Scopus (180) Google Scholar, Suarez et al., 2015Suarez C. Carroll R.T. Burke T.A. Christensen J.R. Bestul A.J. Sees J.A. James M.L. Sirotkin V. Kovar D.R. Profilin regulates F-actin network homeostasis by favoring formin over Arp2/3 complex.Dev. Cell. 2015; 32: 43-53Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar). Thus, by regulating the level of profiling, either formin- or Arp 2/3-generated structures will take up most of the available pool of actin monomers. This observation is consistent with our calculations since we find that when two structures are competing for the same subunit pool the one that assembles faster takes up practically all subunits. Still, further experiments need to be performed in which size distributions of different structures are measured to quantitatively test predictions of the limiting monomer pool model. Predictions of the limiting-pool mechanism for the case of assembly of three-dimensional structures could be tested in C. elegans early embryo cells, where two nucleoli are assembled from a shared pool of nucleoli particles. These nucleoli grow equally in size up until cell division (∼20 min) (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse size scaling of the nucleolus by a concentration-dependent phase transition.Curr. Biol. 2015; 25: 641-646Abstract Full Text Full Text PDF PubMed Scopus (177) Google Scholar). Measurements of the nucleoli size and how they scale with the size of the nucleus are consistent with predictions of the limiting-pool mechanism (Weber and Brangwynne, 2015Weber S.C. Brangwynne C.P. Inverse si" @default.
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• W2617234315 title "The Limiting-Pool Mechanism Fails to Control the Size of Multiple Organelles" @default.
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