Diffusion vs. direct transport in the precision of morphogen readout
Abstract
Morphogen profiles allow cells to determine their position within a developing organism, but not all morphogen profiles form by the same mechanism. Here, we derive fundamental limits to the precision of morphogen concentration sensing for two canonical mechanisms: the diffusion of morphogen through extracellular space and the direct transport of morphogen from source cell to target cell, for example, via cytonemes. We find that direct transport establishes a morphogen profile without adding noise in the process. Despite this advantage, we find that for sufficiently large values of profile length, the diffusion mechanism is many times more precise due to a higher refresh rate of morphogen molecules. We predict a profile lengthscale below which direct transport is more precise, and above which diffusion is more precise. This prediction is supported by data from a wide variety of morphogens in developing Drosophila and zebrafish.
Introduction
Within developing organisms, morphogen profiles provide cells with information about their position relative to other cells. Cells use this information to determine their position with extremely high precision (Dubuis et al., 2013; Erdmann et al., 2009; Gregor et al., 2007a; Houchmandzadeh et al., 2002; de Lachapelle and Bergmann, 2010). However, not all morphogen profiles are formed via the same mechanism and for some profiles the mechanism is still not well understood. One wellknown mechanism is the synthesisdiffusionclearance (SDC) model in which morphogen molecules are produced by localized source cells and diffuse through extracellular space before degrading or being internalized by target cells (Akiyama and Gibson, 2015; Gierer and Meinhardt, 1972; Lander et al., 2002; Müller et al., 2013; Rogers and Schier, 2011; Wilcockson et al., 2017). Alternatively, a direct transport (DT) model has been proposed where morphogen molecules travel through protrusions called cytonemes directly from the source cells to the target cells (Akiyama and Gibson, 2015; Bressloff and Kim, 2018; Kornberg and Roy, 2014; Müller et al., 2013; Wilcockson et al., 2017). The presence of these two alternative theories raises the question of whether there exists a difference in the performance capabilities between cells utilizing one or the other.
Experiments have shown that morphogen profiles display many characteristics consistent with the SDC model. The concentration of morphogen as a function of distance from the source cells has been observed to follow an exponential distribution for a variety of different morphogens (Driever and NüssleinVolhard, 1988; Houchmandzadeh et al., 2002). The accumulation times for several morphogens in Drosophila have been measured and found to match the predictions made by the SDC model (Berezhkovskii et al., 2011). In zebrafish, the molecular dynamics of the morphogen Fgf8 have been measured and found to be consistent with Brownian diffusion through extracellular space (Yu et al., 2009). Despite these consistencies, recent experiments have lent support to the theory that morphogen molecules are transported through cytonemes rather than extracellular space. The establishment of the Hedgehog morphogen gradient in Drosophila is highly correlated in both space and time with the formation of cytonemes (Bischoff et al., 2013), while Wnt morphogens have been found to be highly localized around cell protrusions such as cytonemes (Huang and Kornberg, 2015; Stanganello and Scholpp, 2016). Theoretical studies of both the SDC and DT models have examined these measurable effects (Berezhkovskii et al., 2011; Bressloff and Kim, 2018; Shvartsman and Baker, 2012; Teimouri and Kolomeisky, 2016a), but direct comparisons between the two models have thus far been poorly explored. In particular, it remains unknown whether one model allows for a cell to sense its local morphogen concentration more precisely than the other given biological parameters such as the number of cells or the characteristic lengthscale of the profile.
Here, we derive fundamental limits to the precision of morphogen concentration sensing for both the SDC and DT models. We investigate the hypothesis that sensory precision plays a major role in the selection of a gradient formation mechanism during evolution, and we test this hypothesis by quantitatively comparing our theory to morphogen data. Intuitively one might expect the DT model to have less noise due to the fact that molecules are directly deposited at their target. Indeed, we find below that the noise arises only from molecular production and degradation, with no additional noise from molecular transport. However, we also find below that for sufficiently large morphogen profile lengthscales, the SDC model produces less noise than the DT model due to it being able achieve a higher effective unique molecule count. By elucidating the competing effects of profile amplitude, steepness, and noise, we ultimately conclude that there should exist a profile lengthscale below which the DT model is more precise and above which the SDC mechanism is more precise. We find that this prediction is quantitatively supported by data from a wide variety of morphogens, suggesting that readout precision plays an important role in determining the mechanisms of morphogen profile establishment.
Results
Several past studies have focused on the formation dynamics of morphogen profiles (Berezhkovskii et al., 2011; Bressloff and Kim, 2018; Shvartsman and Baker, 2012; Teimouri and Kolomeisky, 2016a). Here, we model profiles in the steady state regime, as most of the experimental measurements to which we will later compare our results were taken during stages when the steady state approximation is valid (Grimm et al., 2010; Gregor et al., 2007b; Kicheva et al., 2007; Yu et al., 2009; Kanodia et al., 2009). Precision depends not only on stochastic fluctuations in the morphogen concentration, but also on the shape of the mean morphogen profile, as the shape determines concentration differences between adjacent cells that may adopt different fates. Therefore, as in past studies (Gregor et al., 2007a; Tostevin et al., 2007), we define the precision as $P=\mathrm{\Delta}{m}_{j}/{\sigma}_{j}$, where ${\sigma}_{j}$ is the standard deviation of the number of morphogen molecules arriving at cell j, and $\mathrm{\Delta}{m}_{j}={m}_{j}{m}_{j+1}$ is the difference between the molecule number in that cell and the adjacent cell. As is typical in studies of both the DT (Teimouri and Kolomeisky, 2016a; Bressloff and Kim, 2018) and SDC (Berezhkovskii et al., 2011; Shvartsman and Baker, 2012; Teimouri and Kolomeisky, 2016b) mechanisms, we focus on a onedimensional line of target cells. However, we derive analogous results for 2D and 3D systems, and we generally find that the dimensionality does not qualitatively change our results, as we discuss later. In 1D, cells extend in both directions from the source cell, with N cells on each side (Figure 1). We note that in the case of the Bicoid morphogen in the Drosophila embryo, target cells extend only on one side of the source. This will introduce a factor of 2 in the means of both the DT and SDC models and a factor slightly greater than 2 in the variance of the SDC model. This will not affect the agreement of the Bicoid data with our theory in Figure 4B.
A common method for determining the statistical properties of stochastic systems is to express the dynamics of their probability distributions in the form of a master equation. The first moment of the master equation then dictates the dynamics of the mean and becomes the rate equation. Higher order moments can be similarly used to calculate other statistical properties such as the variance. As we will show, in the case of the DT mechanism only the rate equation will be needed as the relevant statistical properties are identical to that of a simple birthdeath system which is fully characterized by its mean. Conversely, the SDC mechanism has more complicated noise properties, which we will calculate via the first and second moments. Specifically, we will use a Langevin description which is expressed as a rate equation with noise terms Gardiner, 2004.
Direct transport model
We first consider the DT case, where morphogen molecules are transported via cytonemes that connect a single source cell to multiple target cells (Figure 1A). Cytonemes are tubular protrusions that are hundreds of nanometers thick and between several and hundreds of microns long (Kornberg and Roy, 2014; Kornberg, 2014). They are supported by actin filaments, and it is thought that morphogen molecules are actively transported along the filaments via molecular motors (Kornberg and Roy, 2014; Kornberg, 2014; Sanders et al., 2013; Huang and Kornberg, 2015). It was recently shown that a DT model that includes forward and backward transport of molecules within cytonemes reproduces experimentally measured accumulation times (Teimouri and Kolomeisky, 2016a; Bressloff and Kim, 2018), although the noise properties of this model were not considered. Here, we review the steady state properties of this model and derive its noise properties.
Consider a single source cell that produces morphogen at rate $\beta $. Morphogen molecules enter each cytoneme at rate $\gamma $. The cytoneme that leads to the jth target cell has length $2ja$, where a is the cell radius. Once inside a cytoneme, morphogen molecules move forward toward the target cell with velocity ${v}^{+}$ or backwards toward the source cell with velocity ${v}^{}$, and can switch between these states with rates ${\zeta}^{+}$ (forwardtobackward) or ${\zeta}^{}$ (backwardtoforward). Once a molecule reaches the forward (backward) end of the cytoneme, it is immediately absorbed into the target (source) cell. Molecules within a target cell spontaneously degrade with rate $\nu $. An alternative model could involve neglecting this degradation step by counting the arrival of morphogen molecules rather than the concentration. This method can at best reduce the variance by a factor of 2 as the noise from degradation is eliminated but the noise from arrival remains. This would cause negligible change to our results presented in Figure 3 and Figure 4 given the order of magnitude difference in precision seen between our two models.
The dynamics of the mean number of morphogen molecules in the source cell ${m}_{0}(t)$ and jth target cell ${m}_{j}(t)$, and the mean density of forwardmoving molecules ${u}_{j}^{+}(x,t)$ and backwardmoving molecules ${u}_{j}^{}(x,t)$ in the jth cytoneme are (Bressloff and Kim, 2018)
where the summation in the first line runs from $j=N$ to $j=N$ excluding 0 (the source cell). Additionally, the boundary conditions ${v}^{+}{u}_{j}^{+}(0,t)=\gamma {m}_{0}(t)$ and ${v}^{}{u}_{j}^{}({L}_{j},t)=0$ are imposed to reflect the rate at which morphogen molecules enter the cytoneme from either end. This creates a steadystate solution of the form
This solution is identical to that found in Bressloff and Kim, 2018 with the exception of the factor of $1/2$ that accounts for target cells existing on both sides of the source cell in our model. Here, $\gamma {\mathrm{\Gamma}}_{j}$ is the effective transport rate of morphogen molecules to the jth target cell, and $\varphi =\mathrm{log}({d}^{}/{d}^{+})$ and $\kappa ={({d}^{+})}^{1}{({d}^{})}^{1}$ are defined in terms of the average distance a molecule would move forward ${d}^{+}={v}^{+}/{\zeta}^{+}$ or backward ${d}^{}={v}^{}/{\zeta}^{}$ within a cytoneme before switching direction. The parameter $\varphi $ sets the shape of ${\mathrm{\Gamma}}_{j}$, and thus of m_{j}: when $\varphi \ll 1$ the profile is constant, ${\mathrm{\Gamma}}_{j}=1$; when $\varphi \gg 1$ it is exponential, ${\mathrm{\Gamma}}_{j}={e}^{2\leftj\right\kappa a}$; and when $\varphi \ll 1$ it is a power law for large j, ${\mathrm{\Gamma}}_{j}={(1+2\leftj\righta/{d}^{+})}^{1}$. The parameter $\kappa $ sets the lengthscale of the profile, defined as
where we approximate the sum as an integral for $N\gg 1$. We use this expression to eliminate $\kappa $, writing ${\mathrm{\Gamma}}_{j}$ in Equation 2 entirely in terms of $\varphi $ and $\widehat{\lambda}\equiv \lambda /a$.
Despite the complexity of the transport process in Equation 1, we find that it adds no noise to m_{j}. In fact, here we prove that any system in which molecules can only degrade in the target cells and cannot leave the target cells has the steadystate statistical properties of a simple birthdeath process. First, consider the special case of only one target cell. Because each morphogen molecule produced in the source cell acts independently of every other morphogen molecule, we define $p(\tau )$ as the probability density that any given molecule will enter the target cell a time $\tau $ after it is created in the source cell. Next, we define $Q(\delta t)$ as the probability that a morphogen molecule will enter the target cell between t and $t+\delta t$. This event requires the molecule to have been produced between $t\tau $ and $t(\tau +d\tau )$, which occurs with probability $\beta d\tau $; to arrive at the target cell a time $\tau $ later and to enter the target cell within the window $\delta t$, which occurs with probability $p(\tau )\delta t$; and we must integrate over all possible times $\tau $. Therefore,
where the last step follows from normalization.
This generalized version of the DT model is visualized in Figure 2. We see that regardless of the form of $p(\tau )$, the probability of a morphogen molecule entering the target cell in any given small time window $\delta t$ is simply $\beta \delta t$. Since the mechanism by which morphogen molecules go from the source cell to the target cell can only affect $p(\tau )$, this result holds regardless of the specifics of the mechanism so long as the condition that the morphogen cannot leave the target cell other than by degradation is maintained. This result also holds when the system is expanded to have multiple target cells, as then $p(\tau )$ is replaced with ${p}_{j}(\tau )$, the probability density that the molecule enters the jth target cell a time $\tau $ after being produced. In this case, ${\int}_{0}^{\mathrm{\infty}}\mathit{d}\tau {p}_{j}(\tau )$ evaluates to ${\pi}_{j}$, the total probability the morphogen molecule is ultimately transported to the jth target cell, and $\beta \delta t$ is simply replaced with $\beta {\pi}_{j}\delta t$. Combined with the constant degradation rate $\nu $ of morphogen molecules within the target cell, this is precisely a birthdeath process with birth rate $\beta {\pi}_{j}$ and death rate $\nu $. For our system ${\pi}_{j}={\mathrm{\Gamma}}_{j}/2{\sum}_{k=1}^{N}{\mathrm{\Gamma}}_{k}$ in Equation 2. The conditions that morphogen molecules act independently and can only degrade within the target cell are critical as the former ensures the noise sources are linear and the later will be violated by the SDC mechanism.
We now assume that each cell integrates its morphogen molecule count over a time T (Berg and Purcell, 1977; Gregor et al., 2007a). The variance in the time average ${T}^{1}{\int}_{0}^{T}\mathit{d}t{m}_{j}(t)$ is simply that of a birthdeath process, given by ${\sigma}_{j}^{2}=2{m}_{j}/(T/\tau )$ (Fancher and Mugler, 2017), so long as $T\gg \tau $, where $\tau ={\nu}^{1}$ is the correlation time. We see that, as expected for a timeaveraged Poisson process, the variance increases with the mean m_{j} and decreases with the number $T/\tau $ of independent measurements made in the time T. The precision is therefore
We see that the precision increases with the profile amplitude m_{j}, the number of independent measurements $T/\tau $, and the profile steepness $\mathrm{\Delta}{m}_{j}/{m}_{j}$. The transport process influences the precision only via m_{j}, not $\tau $. For a given N, j, and $\widehat{\lambda}$, we find that the precision is maximized at a particular ${\varphi}^{*}>0$ (Figure 3A). The reason is that an exponential profile ($\varphi \gg 1$) has constant steepness but small amplitude, whereas a powerlaw profile ($\varphi \ll 1$) has low steepness but large amplitude due to its long tail; the optimum is in between.
SDC model
We next consider the SDC case (Figure 1B). Again a single source cell at the origin $x=0$ produces morphogen at rate $\beta $. However, now morphogen molecules diffuse freely along x with coefficient D and degrade spontaneously at any point in space with rate $\nu $. The dynamics of the morphogen concentration $c(x,t)$ are
where the noise terms associated with diffusion, degradation, and production obey
respectively (Gardiner, 2004; Gillespie, 2000; Fancher and Mugler, 2017; Varennes et al., 2017). Here, the time independent $c(x)=\beta {e}^{\leftx\right/\lambda}/(2\nu \lambda )$ is the steady state mean concentration, with characteristic lengthscale ${\lambda}_{\text{SDC}}=\sqrt{D/\nu}$. We imagine a target cell located at x that is permeable to the morphogen and counts the number $m(x,t)={\int}_{V}\mathit{d}yc(x+y,t)$ of morphogen molecules within its volume V. We use this simpler prescription over explicitly accounting for more realistic mechanisms such as surface receptor binding because it has been shown that the two approaches ultimately yield similar concentration sensing results up to a factor of order unity (Berg and Purcell, 1977). For a cell in steady state at position $x=2ja$, the integral evaluates to
Importantly, in the model presented here the morphogen molecules can diffuse both into and out of the target cells, thus violating the condition that they can only degrade once in a target cell and disallowing our previous argument depicted in Figure 2. However, because Equation 6 is linear with Gaussian white noise, calculating the timeaveraged variance ${\sigma}_{j}^{2}$ is straightforward: we Fourier transform Equation 6 in space and time, calculate the power spectrum of $m(x,t)$, and take its lowfrequency limit (Appendix 1). So long as $T\gg {\nu}^{1}$, we obtain the same functional form as Equation 5,
because diffusion is a Poisson process. This result is distinguished from that of the DT system by the correlation time taking the form
The factor in brackets is always less than one and decreases with $\widehat{\lambda}$. It reflects the fact that, unlike in the DT model, molecules can leave a target cell not only by degradation, but also by diffusion. Therefore, the rate ${\tau}^{1}$ at which molecules are refreshed is larger than that from degradation alone. This effect increases the precision because more independent measurements ($T/\tau $) can be made.
To understand this effect more intuitively, consider a simplified SDC model in which diffusion is modeled as discrete hopping between adjacent target cells at rate h. The autocorrelation function is ${C}_{j}(t)={m}_{j}{I}_{0}(2ht){e}^{(2h+\nu )t}$ (Appendix 2), where I_{0} is the zeroth modified Bessel function of the first kind. The correlation time is $\tau ={\int}_{0}^{\mathrm{\infty}}\mathit{d}t{C}_{j}(t)/{C}_{j}(0)={[\nu (4h+\nu )]}^{1/2}$, and we see explicitly that it decreases with both degradation ($\nu $) and diffusion (h). In fact, in the limit of fast diffusion ($h\gg \nu $), the expression becomes $\tau ={(4\nu h)}^{1/2}$. Correspondingly, in the fastdiffusion limit of Equation 9 ($\widehat{\lambda}\gg 1$), the term in brackets reduces to ${\widehat{\lambda}}^{1}$, and it becomes $\tau ={(\nu \widehat{\lambda})}^{1/2}={[4\nu D/{(2a)}^{2}]}^{1/2}$. These expressions are identical, with $D/{(2a)}^{2}$ playing the role of the hopping rate h, as expected.
Comparing the models
We now ask which model has the higher precision. We first note that while the precision in both models has the same form when expressed in terms of the means and correlation times (Equations 5 and 8), substituting in the corresponding expressions reveals how the precision depends on the various parameters of each model. Specifically, the precision in the DT model is seen to depend on N, j, the product $\beta T$, $\widehat{\lambda}$, and $\varphi $. Importantly, it is independent of $\nu $ so long as the condition $T\gg {\tau}_{\mathrm{DT}}={\nu}^{1}$ is met. This is due to the mean molecule count scaling as ${\nu}^{1}$ (Equation 2) and the number of independent measurements ($T/{\tau}_{\mathrm{DT}}=\nu T$) scaling as $\nu $, thus causing their product and in turn the precision to be independent of $\nu $. In the SDC model, the precision depends on j, the product $\beta T$, and $\widehat{\lambda}$. For similar reasons as in the DT model, $\nu $ does not explicitly appear once Equations 7 and 9 are inserted into Equation 8, but it is present implicitly in the definition of $\widehat{\lambda}=\sqrt{D/\nu}/a$.
To properly compare the two models, we equate several variables. Specifically, we wish to compare the precision of each model within a specific system, which requires N and j to be the same in both models. Additionally, $\beta $ and $\nu $ are restricted by the energy and material costs of producing and degrading morphogen molecules while T is restricted by the need of the system to properly develop in a finite amount of time. These restrictions are assumed to be independent of the method of morphogen profile establishment, which implies that both the DT and SDC systems will adopt the same maximal values of $\beta $ and T. While the value of $\nu $ is restricted in a similar manner, explicitly equating it between the two models does not reduce the number of free parameters, as the precision is independent of $\nu $ in the DT model, and $\nu $ is insufficient to fully define the value of $\widehat{\lambda}$ in the SDC model. Therefore, we equate the characteristic profile length scale $\widehat{\lambda}$, as this parameter is consistently defined across both models and is also measured in experiments, allowing us to compare our theory with data as discussed below. Finally, we optimize over $\varphi $ as it is unique to the DT model.
We now observe how the precision of the DT and SDC models compare in a representative system. Figure 3B shows ${\rho}_{j}={P}_{\mathrm{DT}}^{2}/{P}_{\mathrm{SDC}}^{2}$ as a function of profile length $\widehat{\lambda}$ for a cell in the center ($j=N/2$) of a line of $N=100$ target cells, where for each $\widehat{\lambda}$ we use the ${\varphi}^{*}$ that maximizes ${P}_{\mathrm{DT}}^{2}$ as seen in Figure 3A. Since $\beta $ and T have been equated between the two models, ${\rho}_{j}$ is independent of both. We see that for short profiles the DT model is more precise (${\rho}_{j}>1$) whereas for long profiles the SDC model is more precise (${\rho}_{j}<1$). This effect holds for a single source cell providing morphogen for a 1D line of target cells as well as for a 1D line of source cells with a 2D sheet of target cells and a 2D sheet of source cells with a 3D volume of target cells. For the DT model, the 2D and 3D cases are identical to the 1D case as we assume that cytonemes extend perpendicular to the source cells; for the SDC model see Appendix 1.
The reason that the SDC model is more precise for long profiles is that long profiles correspond to fast diffusion, which increases the refresh rate ${\tau}_{\mathrm{SDC}}^{1}$ as discussed above. Conversely, the reason that the DT model is more precise for short profiles is that it has a larger amplitude. It also has a smaller steepness, but the larger amplitude wins out. Specifically, whereas the SDC amplitude falls off exponentially, ${m}_{j}\sim {e}^{2\leftj\right/\widehat{\lambda}}$, for sufficiently small ${\varphi}^{*}$ the DT amplitude falls off as a power law, ${m}_{j}\sim 1/\leftj\right$. The steepness $\mathrm{\Delta}{m}_{j}/{m}_{j}$ of the SDC profile is constant, while the steepness of the DT profile also scales like $1/j$. Thus, the product of the ratio of amplitudes and the square of the ratio of steepnesses, on which ${\rho}_{j}$ depends, scales like ${e}^{2\leftj\right/\widehat{\lambda}}/{\leftj\right}^{3}$. For small $\widehat{\lambda}$, the exponential dominates over the cubic for the majority of j values. Consequently, the DT model has the higher precision.
Comparison to data
We now test our predictions against data for various morphogens. In Drosophila, the morphogen Wingless (Wg) is localized near cell protrusions such as cytonemes (Huang and Kornberg, 2015; Stanganello and Scholpp, 2016), and the Hedgehog (Hh) gradient correlates highly in both space and time with the formation of cytonemes (Bischoff et al., 2013), suggesting that these two morphogen profiles are formed via a DT mechanism. Conversely, Bicoid has been understood as a model example of SDC for decades (Driever and NüssleinVolhard, 1988; Gregor et al., 2007a; Houchmandzadeh et al., 2002). Similarly, Dorsal is spread by diffusion; however, its absorption is localized to a specific region of target cells via a nonuniform degradation mechanism, making it more complex than the simple SDC model (Carrell et al., 2017). Finally, for Dpp there is evidence for a variety of different gradient formation mechanisms (Akiyama and Gibson, 2015; Müller et al., 2013; Wilcockson et al., 2017).
In zebrafish, the morphogen Fgf8 has been studied at the single molecule level and found to have molecular dynamics closely matching the Brownian movement expected in an SDC mechanism (Yu et al., 2009). Similarly, Cyclops, Squint, Lefty1, and Lefty2, all of which are involved in the Nodal/Lefty system, have been shown to spread diffusively and affect cells distant from their source (Müller et al., 2013; Rogers and Müller, 2019). This would support the SDC mechanism, although Cyclops and Squint have been argued to be tightly regulated via a GiererMeinhardt type system, thus diminishing their gradient sizes to values much lower than what they would be without this regulation (Gierer and Meinhardt, 1972; Rogers and Müller, 2019).
For all these morphogens, we estimate the profile lengthscales $\lambda $ from the experimental data (Kicheva et al., 2007; Wartlick et al., 2011; Gregor et al., 2007b; Liberman et al., 2009; Yu et al., 2009; Müller et al., 2012) (Appendix 3). Figure 4A shows these $\lambda $ values and indicates for each morphogen whether the evidence described above suggests a DT mechanism (red), an SDC mechanism (blue), or multiple mechanisms including DT and SDC (white). We see that in general, the three cases correspond to short, long, and intermediate profile lengths, respectively, which is qualitatively consistent with our predictions.
To make the comparison quantitative, we estimate the values of cell radius a and cell number N from the experimental data (Kicheva et al., 2007; Gregor et al., 2007a; Liberman et al., 2009; Yu et al., 2009; Kimmel et al., 1995) (Appendix 3) in order to calculate ${\rho}_{j}$ from our theory in each case. The background color in Fig. Figure 4B shows the percentage of cells within each system for which we predict that the SDC model is more precise as a function of $\widehat{\lambda}$. The data points in Fig. Figure 4B show the values of $\widehat{\lambda}$ from the experiments, also normalized by ${\widehat{\lambda}}_{50}$, the value of $\widehat{\lambda}$ at which 50% of cells are more precise in SDC, from the theory. For each morphogen species, we assume a 1D system for simplicity as we have checked that considering higher dimensions yields negligible differences to the results presented in Figure 4B. We see that our theory predicts the correct threshold: the morphogens for which the evidence suggests either a DT or an SDC mechanism (red or blue) fall into the regime in which we predict that mechanism to be more precise for most of the cells, and the morphogens with multiple mechanisms (white) fall in between. This result provides quantitative support for the idea that morphogen profiles form according to the mechanism that maximizes the sensory precision of the target cells.
Discussion
We have shown that in the steadystate regime, the DT and SDC models of morphogen profile formation yield different scalings of readout precision with the length of the profile and population size. As a result, there exist regimes in this parameter space in which either mechanism is more precise. While the DT model benefits from larger molecule numbers and no added noise from the transport process, the ability of molecules to diffuse into and away from a target cell in the SDC model allows the cell to measure a greater number of effectively unique molecules in the same time frame. By examining how these phenomena affect the cells’ sensory precision, we predicted that morphogen profiles with shorter lengths should utilize cytonemes or some other form of direct transport mechanism, whereas morphogens with longer profiles should rely on extracellular diffusion, a prediction that is in quantitative agreement with measurements on known morphogens. It will be interesting to observe whether this trend is further strengthened as more experimental evidence is obtained for different morphogens, as well as to expand the theory of multicellular concentration sensing to further biological contexts.
Despite the quantitative agreement between our theory and experiments, it is clear that the models presented here are minimal and thus cannot be directly applied to all systems. This is exemplified by morphogen such as Dorsal, which due to aforementioned diffusive spreading and nonuniform degradation mechanism clearly does not strictly follow either model. Additionally, the SDC model can be violated if the diffusion of morphogen through a biological environment is hindered by the typically crowded nature of such environments, leading to possibly subdiffusive behavior (Ellery et al., 2014; Fanelli and McKane, 2010). For the DT model, we explicitly ignored the dynamics of the cytonemes themselves due to the growth rate of the cytonemes being sufficiently fast so as to traverse the entire system size in significantly less time than is required for the cells to integrate their morphogen counts over (Bischoff et al., 2013; Chen et al., 2017). This assumption is problematic if cytonemes continue to behave dynamically after reaching the source cell. In particular, the process of cytonemes switching between phases of growing and retracting can introduce superPoissonian noise sources to the morphogen count within the target cells. SuperPoissonian noise can also be introduced by relaxing the assumption that the morphogen molecules behave independently of each other as this was a critical component of our proof that the molecule count in the target cells is statistically identical to a birthdeath process. Finally, it is conceivable that a hybrid system which utilizes a combination of diffusion and directed transport could be developed. However, we are unaware of any experimental study into such a possibility and as such have not considered it in this particular work. It will be interesting to explore the implications of each of these complications in future works.
Materials and methods
Methods are described in Appendices 1, 2, and 3 along with more detailed evaluations of 2D and 3D SDC systems and further discussion of data used in Figure 4. Additionally, code used to generate the plots seen in Figure 3 and Figure 4 can be found at Fancher, 2020.
Appendix 1
Timeaveraged variance in the SDC model
Here we calculate the timeaveraged variance of the morphogen molecule number using the lowfrequency limit of the power spectrum. We first introduce the power spectrum, and then we calculate the variance for the 1D, 2D, and 3D geometries.
Power spectrum
We first discuss the correlation function and power spectrum to establish some definitions and notation. Specifically, we show that the variance in the longtime average of a variable is given by the lowfrequency limit of its power spectrum. For a onedimensional function $x(t)$ with mean 0, the correlation function $C\left(t\right)$ takes the form
Since absolute time is irrelevant in the steady state of any physical system with no time dependent forcing, ${t}^{\prime}$ can be set to 0 without loss of generality. This leads to a definition for the power spectrum of $x(t)$ as
Thus, under this definition the power spectrum is seen to be the Fourier transform of the correlation function. Additionally, when $x\left(t\right)$ is averaged over a time T, the time averaged correlation function of $x\left(t\right)$ takes the form
Let $y\equiv \left(\tau {\tau}^{\prime}\right)\left(t{t}^{\prime}\right)$ and $z\equiv \left(\tau +{\tau}^{\prime}\right)\left(t+{t}^{\prime}\right)$. This transforms Equation 13 into
By inverting the relationship found in Equation 12, $C\left(y+t{t}^{\prime}\right)$ can be replaced with an inverse Fourier transform of $S\left(\omega \right)$ to produce
The factor of ${\left(\omega T\right)}^{2}$ in the integrand of Equation 15 forces only small values of $\omega $ to contribute when T is large. Thus, the approximation $S\left(\omega \right)\approx S\left(0\right)$ can be made since only values of $\omega $ near 0 are contributing. This causes ${C}_{T}\left(0\right)$, which we will denote as ${\sigma}^{2}$ through this and the main text, to be exactly calculable to
Of important note is the fact that this approximation only works if $S(\omega )$ varies slowly compared to ${(2\mathrm{sin}(\omega T/2)/\omega T)}^{2}$ near $\omega =0$. Since $C(t)$ must be time symmetric, $S(\omega )$ must also be symmetric and thus an even function of $\omega $. Thus, near $\omega =0$ the lowest order correction term for each function will be the second order term. Normalizing each term by the 0frequency value of each function then lets us to impose the condition
So long as this condition is satisfied, the approximation given in Equation 16 is valid.
We now cast Equation 16 into a more intuitive form by considering the correlation time $\tau $, which can be defined as
Continuing the use the fact that $C\left(t\right)$ must be time symmetric and thus an even function of t, Equation 12 can be used to produce the result
Inserting this result into Equation 16 produces
thus relating the longtime averaged variance, ${\sigma}^{2}$, to the instantaneous variance, $C\left(0\right)$, and the number of correlation times the system averages over, $T/\tau $.
Variance and precision
We now consider a model for the SynthesisDiffusionClearance system. We still assume there is a single source cell which produces morphogen at rate $\beta $, but now the morphogen is released into the extracellular environment where it freely diffuses at rate D. The morphogen can also spontaneously degrade at rate $\nu $. Even though in the main text we focus on a zerodimensional source in a onedimensional space, here we will look at diffusion in a multitude of different spaces with different dimensions as well as morphogen sources that span a multitude of different dimensions. In each case, the sources will secrete morphogen molecules into a density field c which must follow
where $SP$ is the number of spatial dimensions, $SO$ is the dimensionality of the source, and ${\nabla}^{2}$ is taken over all $SP$ dimensions. Each $\eta $ term is a Langevin noise term that represents Gaussian white noise for the diffusion, degradation, and production processes respectively. Of important note is that ${\delta}^{SPSO}\left(\overrightarrow{x}\right)$ is a $\delta $ function only in the last $SPSO$ dimensions of the space. So, for example, if there was a one dimensional source in three dimensional space, then ${\delta}^{31}\left(\overrightarrow{x}\right)$ would be a $\delta $ function in the $\widehat{y}$ and $\widehat{z}$ directions but not the $\widehat{x}$ direction. This means that $\beta $ and ${\eta}_{\beta}$ will have units of ${T}^{1}{L}^{SO}$, where T is time and L is space.
We can now assume c has reached a steady state and separate it into $c=\overline{c}+\delta c$, which in turn allows Equation 21 to separate into
where
Fourier transforming Equation 22 in space and dividing it by $\nu $ then yields
where
Of similarly important note is that ${\delta}^{SO}\left(\overrightarrow{k}\right)$ is a $\delta $ function only in the first $SO$ dimensions of kspace. So in the onedimensional source, threedimensional space example ${\delta}^{SO}\left(\overrightarrow{k}\right)$ would be a $\delta $ function in the $\widehat{x}$ direction of kspace but not the $\widehat{y}$ or $\widehat{z}$ directions.
This allows $\overline{c}$ to be written as
where
It is important to note that ${P}_{N}$ does not integrate over all available dimensions, but only over the last N dimensions of the space. This in turn means that its argument can only depend on the last N dimensions of any input vector. Returning to the one dimensional source, three dimensional space example, ${P}_{31}\left(\left\overrightarrow{x}\right/\lambda \right)$ should only take the y and z components of $\overrightarrow{x}$ into account. The x component is made irrelevant by the translational symmetry of the system along the xaxis.
Moving on to the noise terms, Equation 23 can be Fourier transformed in space and time to yield
where ${\eta}_{\beta}(\overrightarrow{k},\omega )$ depends only on the first $SO$ dimensions of kspace. Assuming the $\eta $ terms are all independent of each other allows the cross spectrum of c to be
The cross spectrum of ${\eta}_{D}$ can be obtained from its correlation function. To derive such a correlation function, we first consider a separate Markovian system comprised of a 1dimensional lattice of discrete compartments that a diffusing species Y can exist in. The dimensionality is chosen purely for simplicity, as the method outlined below can be easily generalized to higher dimensions to produce the same result. Let ${y}_{i}\left(t\right)$ be the number of Y molecules in the ith compartment at time t and d be the rate at which these molecules move to the $i1$ or $i+1$ compartment. Given a sufficiently small time step $\delta t$, the probability of a molecule moving from the ith compartment to the $i\pm 1$ compartment is
Higher order interactions in which multiple molecules are transfered within the time step $\delta t$ will have probabilites of order ${\left(\delta t\right)}^{2}$ or higher and can thus be ignored. This allows the mean of $\delta {y}_{i}\left(t\right)={y}_{i}\left(t+\delta t\right){y}_{i}\left(t\right)$ to take the form
where the first two terms come from molecules moving into theith compartment from the $i1$ and $i+1$ compartments respectively and the third term comes from the two different ways molecules can leave the ith compartment. As $\delta t$ is small, each of these transfer processes can be treated as being Poissonianly distributed. This allows the variance of $\delta {y}_{i}\left(t\right)$ to simply be the righthand side of Equation 24 but with each term taken to be its absolute value so there are no subtractions. Additionally, this approximation allows the covariance between $\delta {y}_{i}$ and $\delta {y}_{i\pm 1}$ to be taken as the negative of the sum of the expected number of molecules moving from the ith compartment to the $i\pm 1$ compartment and vice versa. With these, the correlation function between $\delta {y}_{i}\left(t\right)$ and $\delta {y}_{j}\left(t\right)$ can be written as
We now take the system to continuous space by letting ${y}_{i}\left(t\right)\to \mathrm{\ell}c(x,t)$ and ${\delta}_{i,j}\to \mathrm{\ell}\delta \left(x{x}^{\prime}\right)$ with any intances of ±1 in the indices also being converted to $\pm \mathrm{\ell}$. Putting these substitutions into Equation 25 and dividing by ${\left(\mathrm{\ell}\delta t\right)}^{2}$ yields
Equation 33 has been rearranged into this form so as to easily apply the operators ${\partial}_{x}^{\pm}$ defined as
Using this notation, Equation 33 can be simplfied into
Taking the $\mathrm{\ell}\to 0$ limit while holding $D={\mathrm{\ell}}^{2}d$ constant allows ${\partial}_{x}^{\pm}$ and ${\partial}_{{x}^{\prime}}^{\pm}$ to converge to true derivatives, ${\partial}_{x}$ and ${\partial}_{{x}^{\prime}}$. Additionally, if the $\delta c({x}^{\prime},t)/\delta t$ term on the lefthand side of Equation 35 is replaced with $\delta c({x}^{\prime},{t}^{\prime})/\delta t$ for ${t}^{\prime}\ne t$, then the entire righthand side must go to 0 as the system is Markovian. This can be accomplished by multiplying the righthand side by a factor of ${\delta}_{t,{t}^{\prime}}$. Taking the $\delta t\to 0$ limit then turns the two terms on the lefthand side into true derivatives in time, ${\partial}_{t}$ and ${\partial}_{{t}^{\prime}}$, acting on $c(x,t)$ and $c({x}^{\prime},{t}^{\prime})$ respectively while the factor of ${\delta}_{t,{t}^{\prime}}/\delta t$ on the righthand side becomes $\delta \left(t{t}^{\prime}\right)$. Altogether, this transforms Equation 35 into
Finally, by approximating the system as being in steady state, $c(x,t)$ can be replaced with $\overline{c}\left(x\right)$ and ${\partial}_{t}c(x,t)$ becomes equivalent to ${\eta}_{D}(x,t)$. Making these substitutions and generalizing Equation 36 to arbitrary dimensions yields
Fourier transforming Equation 37 can be easily performed due to the $\delta $ functions, integrating the spatial terms by parts, and utilizing Equation 24 to yield
Moving on to ${\eta}_{\nu}$, its correlation function must be $\delta $ correlated in time and space since it is a purely local reaction and as such, at steady state, must take the form
Fourier transforming Equation 39 is again easily performed due to the $\delta $ functions and Equation 24. This yields
Finally, the cross spectrum of ${\eta}_{\beta}$ must be $\delta $ correlated in $\omega $space as well as all source dimensions of kspace since it is merely a uniform production term that does not depend on space or time. This yields
Combining Equations 29, 38, 40, and 41 then yields
We now define m as
where $V\left(a\right)$ is a $SP$dimensional sphere with radius a. This allows the mean value of m to be written as
where
Since ${P}_{N}\left(\left\overrightarrow{x}\right\right)$ can only depend on the last N dimensions of its input vectors, the same must be true of ${M}_{N,{N}^{\prime}}$. From here we define $S\left(\overrightarrow{x}\right)$ as the 0frequency limit of the cross spectrum in $\omega $space of m. This allows the time averaged variance, ${\sigma}^{2}\left(x\right)$ to take the form
where
Wherein once again only the last N dimensions of the input vectors can be taken into account. Combining Equations 44 and 46 yields the full precision to be
where
With Equation 48, once the forms of ${P}_{N}$, ${M}_{N,{N}^{\prime}}$, and ${\mathrm{\Sigma}}_{N,{N}^{\prime}}$ are determined for a given $SP$ and $SO$, the full form of the noisetosignal ratio can be found. We now calculate these forms for specific choices of $SP$ and $SO$.
1D space, 0D source
To begin, we start with the simple scenario in which $SP=1$ and $SO=0$. This allows P_{1}, ${M}_{1,1}$, and ${\mathrm{\Sigma}}_{1,1}$ to take the forms
Equation 51 and Equation 52 can then be put into Equation 48 along with the assumption $\leftx\right>a$ to obtain
and
as in Equations 9 and 10 of the main text.
Next we apply the condition given by Equation 17 to determine the regime in which these results are valid for the $SP=1$ and $SO=0$ case. A similar methodology can be done for each of the other cases we will look at, though this is the only one we do explicitly. To begin, we will reperform the calculation done in Equation 46 but without taking the $\omega \to 0$ limit so as to obtain the full form of $S(\omega ,x)$.
where
and $\sqrt{1+iy}$ is assumed to be the branch with a positive real component. Plugging this and the explicit form of $\overline{c}$ into Equation 55 then yields
where
The function $\mathrm{{\rm Y}}(x,y,w)$ limits to ${\mathrm{\Sigma}}_{1,1}(x,y)$ when $w\to 1$ and as such has different forms when x is less or greater than y. As the purpose of this exercise is to determine the regime in which our theoretical approximations are valid and our model obeys $\leftx\right\ge 2a$ for all cells, here we will only present the $x>y$ solution for simplicity. Using this to perform the integrals in Equation 58 and applying the result toEquation 57 then yields
where $W=\sqrt{1+i\frac{\omega}{\nu}}$, which in turn implies $\omega =i\nu ({W}^{2}1)$. With this, it is easier to perform all further calculations with respect to W and take the $W\to 1$ limit as that is equivalent to the $\omega \to 0$ limit.
We can now combine this with the known form of $S(0,x)$ given in Equation 46 to evaluate Equation 17 to take the form
The righthand side of Equation 60 is a function that monotonically increases from $9/2{\nu}^{2}$ to $21/2{\nu}^{2}$ as $a/\lambda $ goes from 0 to $\mathrm{\infty}$. Thus, regardless of the value of $\lambda $, $\nu $ sets the timescale to which T must be compared.
2D space, 0D source
For $SP=2$ and $SO=0$, P_{2}, ${M}_{2,2}$, and ${\mathrm{\Sigma}}_{2,2}$ each take the form
where ${J}_{n}\left(x\right)$ and ${K}_{n}\left(x\right)$ are the Bessel functions of the first kind and modified Bessel functions of the second kind, respectively. Unfortunately, the complicated nature of Bessel functions makes the remaining integrals unsolvable analytically, and therefore we evaluate them numerically. Similar problems arise whenever $SP=2$ or $SPSO=2$.
3D space, 0D source
For $SP=3$ and $SO=0$, P_{3}, ${M}_{3,3}$, and ${\mathrm{\Sigma}}_{3,3}$ each take the form
2D space, 1D source
For $SP=2$, $SO=1$, P_{1} and ${M}_{2,2}$ are known from Equations 50 and Equations 62. This leaves ${M}_{1,2}$ and ${\mathrm{\Sigma}}_{1,2}$ to take the forms
Again, we evaluate the remaining integrals numerically.
3D space, 2D source
For $SP=3$, $SO=2$, P_{1} and ${M}_{3,3}$ are known from Equations 50 and Equations 65. This leaves ${M}_{1,3}$ and ${\mathrm{\Sigma}}_{1,3}$ to take the forms
Appendix 2
Hopping model for SDC case
To obtain a more intuitive understanding of why the SDC model results in the scaling properties seen in the various calculations of ${M}_{\text{SP}\text{SO},\text{SP}}$ and ${\mathrm{\Sigma}}_{\text{SP}\text{SO},\text{SP}}$, we now look at a simpler version of one dimensional diffusion in which we discretize space into compartments of uniform size. Let molecules still be produced in the 0th compartment at rate $\beta $ and degrade anywhere in space at rate $\nu $. The process of diffusion can be approximated by letting the molecules hop to neighboring compartments with rate h with equal probability of moving left or right. This allows the dynamics of m_{j}, the number of molecules in the j compartment for $j\in \mathbb{Z}$, to be written as
By setting the lefthand side of Equation 45 to 0, the resulting system of equations can be easily solved by assuming ${\overline{m}}_{j}=A\text{exp}(2\leftj\right/\lambda )$ and calculating A and $\lambda $. Imposing this assumption on Equation 45 and taking $j>0$ yields
With $\lambda $ solved for, we solve for the proportionality constant by noting that the total number of molecules in the whole system must follow a simple birthdeath process with a mean of $\beta /\nu $. This in turn implies
This in turn gives the average value of m_{j} to be
Next, we calculate the full distribution of m_{j} by assuming that at any given moment in time each molecule in the system has probability ${P}_{j}$ of being in the jth compartment. This can be combined with the aforementioned fact that N, the total number of molecules in the system, must follow a birthdeath process and thus to Poissonianly distributed with mean $\beta /\nu $. For any given value of N, $P({m}_{j}N)$ must be a binomial distribution with success probability ${P}_{j}$ since each molecule is independent. This allows the marginal distribution $P({m}_{j})$ to be calculated to be
Thus, m_{j} is seen to be Poissonianly distributed with mean $\beta {P}_{j}/\nu $. Comparing this mean to that derived in Equation 46 then implies
We now consider the joint distribution of m_{j} and m_{k} for $j\ne k$. Since molecules cannot be in the jth and kth compartment simultaneously, the joint conditional distribution $P({m}_{j},{m}_{k}N)$ must be trinomially distributed. This allows for the joint distribution to be calculated in a manner similar to Equation 75 to produce
Thus, the joint probability distribution of m_{j} and m_{k} is seen to be separable into the product of the two marginal distribution, meaning that sametime, instantaneous measurements of m_{j} an m_{k} must be uncorrelated.
From here we can begin to calculate the full correlation function for m_{j} and m_{k}. We start by defining $\delta {m}_{j}(t)={m}_{j}(t){\overline{m}}_{j}$ and $\delta {m}_{k}(t)={m}_{k}(t){\overline{m}}_{k}$. Since ${\overline{m}}_{j}$ is known to set the righthand side of Equation 45 to 0, the dynamics of $\delta {m}_{j}$ can be written as
with the same being true for $\delta {m}_{k}$. Additionally, we assume the system is at steady state so that all mean expressions are invariant to time translation. Given this, we can without loss of generality take the correlation function between $\delta {m}_{j}$ and $\delta {m}_{k}$ to have the form
where $t>0$. Applying the dynamic result given in Equation 78 then yields
The final form of Equation 80 can be split into the term $(2h+\nu ){C}_{j,k}$ which implies ${C}_{j,k}\propto \text{exp}((2h+\nu )t)$ and the term $h({C}_{j,k+1}+{C}_{j,k1})$ which is the recursion relation for ${I}_{\mathrm{\ell}}(2ht)$, the modified Bessel function of the first kind, where $\mathrm{\ell}$ is some function of j and k. This means ${C}_{j,k}(t)$ can be written as
for some proportionality constant A.
To determine the forms of A and $\mathrm{\ell}(j,k)$, we can utilize the initial condition that m_{j} is Poissonianlly distributed and thus has a variance equal to its mean while being completely uncorrelated with m_{k} when both are measured at the same time. This means ${C}_{j,k}(0)$ can be written as
which in turn implies $\mathrm{\ell}(j,j)=0$ as ${I}_{n}(0)={\delta}_{0n}$ for $n\in \mathbb{Z}$. To satisfy the recursion relation term of Equation 80, it must then be the case that $\mathrm{\ell}(j,j+n)=n$. Setting $k=j+n$ thus yields $\mathrm{\ell}(j,k)=kj$. Since k and j are integers, $\mathrm{\ell}(j,k)=jk$ is equally valid as ${I}_{n}={I}_{n}$ again for $n\in \mathbb{Z}$. Combining these results together yields the final form of ${C}_{j,k}(t)$ to be
Next, let $\tau $ be the autocorrelation time of m_{j}. This quantity is typically defined by integrating ${C}_{j,j}(t)/{C}_{j,j}(0)$ over all time. Using the known properties of modified Bessel functions, this can be solved to yield
If we now define $M=T/\tau $ where M is the number of effectively independent measurements that can be made in a time T, we see that for $h\gg \nu $, $M\approx 2\sqrt{\nu h}T$. Additionally, from Equation 72 we see that in the $h\gg \nu $ regime $\lambda \approx 2\sqrt{h/\nu}$. By equating this $\lambda $ to the nondimensionalized ${\lambda}_{\text{SDC}}/a$ from the SDC model we see that $M\approx \lambda \nu T=({\lambda}_{\text{SDC}}/a)\nu T$. This is consistent with the fact that for ${\lambda}_{\text{SDC}}\gg a$ the righthand side of Equation 54 becomes approximately $a/{\lambda}_{\text{SDC}}\nu $, which allows $M=T/{\tau}_{SDC}\approx \left({\lambda}_{\text{SDC}}/a\right)\nu T$.
In the $h\ll \nu $ regime, we find $M\approx \nu T$. Once again, this consistent with Equation 54 when ${\lambda}_{\text{SDC}}\ll a$ as this causes the righthand side to become approximately ${\nu}^{1}$. Thus, the SDC model is seen to have a correlation time that agrees with Equation 84 in both the large and small h regime.
Appendix 3
Comparison to experimental data
To compare our theory to experimental data, we focus on ten of the morphogens presented in Table 1 of Kicheva et al., 2012 and obtain data from the references therein. For Bicoid, we obtain a value of $\lambda $ of ∼100 µm from the text of Gregor et al., 2007b with and error of ±10 µm from the finding in Gregor et al., 2007a that cells have a ∼10% error in measuring the Bicoid gradient. We then take the a value of the Drosophila embryo cells that are subjected to the Bicoid gradient to be ∼2.8 µm based on Figure 3A of Gregor et al., 2007a. We use the same figure to estimate the size of the whole embryo to be ∼500 µm or ∼90 cells. This value of a is also used for Dorsal as measurements of both Bicoid and Dorsal occur in the Drosophila embryo at nuclear cycle 14. For the value of $\lambda $ for Dorsal, we use Figure 3D from Liberman et al., 2009 to obtain a full width at 60% max of 45 ± 10 µm. Since this represents the width of Gaussian fit on both sides of the source whereas our model uses an exponential profile, we assume the appropriate $\lambda $ value for such an exponential fit would be half this value, 22.5 ± 5 µm. Figure 3A from the same source also shows that the distance from the ventral midline to the dorsal midline is ∼200 µm or ∼35 cells.
For Dpp and Wg, Kicheva et al., 2007 provides explicit measurements of $\lambda $ for each. These values are 20.2 ± 5.7 µm and 5.8 ± 2.04 µm respectively. For Hh, we use Figure S2C in the supplementary material of Wartlick et al., 2011 to determine $\lambda $ to be 8 ± 3 µm. Dpp, Wg, and Hh all occur in the wing disc during the third instar of the Drosophila development. As such, we use a common value of a for all three. This value is taken to be 1.3 µm based on the area of the cells being reported as 5.5 ± 0.8 µm^{2} in the supplementary material of Kicheva et al., 2007 and the assumption that the cells are circular. Additionally, the scale bar for Figure 1A in Wartlick et al., 2011 shows the maximal distance from the morphogen producing midline of the wing disc to its edge to be ∼250 µm or ∼100 cells.
The $\lambda $ value of Fgf8 is reported as being 197 ± 7 µm in Yu et al., 2009. Additionally, based off the scale bars seen in Figure 2C–E of Yu et al., 2009, we estimate the value of a for the cells to be ∼10 µm. For the morphogens involved in the Nodal/Lefty system (cyclops, squint, lefty1, and lefty2), measurements of $\lambda $ for each are taken from Figure 2C–F of Müller et al., 2012 by observing where the average of the three curves crosses the 37% of max threshold with error bars given by the width of the region in which the vertical error bars of each plot intersect this threshold line. We assume the a value of each morphogen in the Nodal/Lefty system to be equivalent to the a value of cells in the Fgf8 measurements performed in Yu et al., 2009. This is because the measurements made in Müller et al., 2012 were taken during the blastula stage of the zebrafish development while measurements taken in Yu et al., 2009 we taken in the sphere germ ring stage. These stages occur at ∼2.25 and ∼5.67 hpf respectively, but the blastula stage can last until ∼6 hpf based on the timeline of zebrafish development presented in Kimmel et al., 1995. As such, since there is potential overlap in the time frame of these two stages, we assume the cells maintain a relatively fixed size and thus that the value of a for the Nodal/Lefty system can be taken as the same value of a used for Fgf8. Additionally, as seen in Figures 8F and 11B in Kimmel et al., 1995, these two stages also share a rougly equal overall diameter of the embryo of ∼500 µm at the largest point. This creates a circumference of ∼1600 µm or ∼80 cells, which in turn means the morphogen must travel a maximum distance of ∼40 cells away from the source.
Morphogen  Organism  $\lambda $ (µm)  a (µm)  N 

Bicoid  Drosophila  100 ± 10  2.8  90 
Fgf8  Zebrafish  197 ± 7  10  40 
Lefty2  Zebrafish  150 ± 25  10  40 
Lefty1  Zebrafish  115 ± 20  10  40 
Dpp  Drosophila  20.2 ± 5.7  1.3  100 
Dorsal  Drosophila  22.5 ± 5  2.8  35 
Squint  Zebrafish  65 ± 10  10  40 
Cyclops  Zebrafish  30 ± 5  10  40 
Hh  Drosophila  8 ± 3  1.3  100 
Wg  Drosophila  5.8 ± 2.04  1.3  100 
Data availability
All data used in this study is simulated via computational methods outlined in the manuscript and appendices.
References

Morphogen transport: theoretical and experimental controversiesWiley Interdisciplinary Reviews: Developmental Biology 4:99–112.https://doi.org/10.1002/wdev.167

Formation of morphogen gradients: local accumulation timePhysical Review E 83:051906.https://doi.org/10.1103/PhysRevE.83.051906

Physics of chemoreceptionBiophysical Journal 20:193–219.https://doi.org/10.1016/S00063495(77)855446

Precision and scaling in morphogen gradient readoutMolecular Systems Biology 6:351.https://doi.org/10.1038/msb.2010.7

Characterizing transport through a crowded environment with different obstacle sizesThe Journal of Chemical Physics 140:054108.https://doi.org/10.1063/1.4864000

Role of spatial averaging in the precision of gene expression patternsPhysical Review Letters 103:258101.https://doi.org/10.1103/PhysRevLett.103.258101

Fundamental limits to collective concentration sensing in cell populationsPhysical Review Letters 118:078101.https://doi.org/10.1103/PhysRevLett.118.078101

Diffusion in a crowded environmentPhysical Review E 82:021113.https://doi.org/10.1103/PhysRevE.82.021113

The chemical Langevin equationThe Journal of Chemical Physics 113:297–306.https://doi.org/10.1063/1.481811

Dynamics of the dorsal morphogen gradientPNAS 106:21707–21712.https://doi.org/10.1073/pnas.0912395106

Investigating the principles of morphogen gradient formation: from tissues to cellsCurrent Opinion in Genetics & Development 22:527–532.https://doi.org/10.1016/j.gde.2012.08.004

Stages of embryonic development of the zebrafishDevelopmental Dynamics 203:253–310.https://doi.org/10.1002/aja.1002030302

Cytonemes and the dispersion of morphogensWiley Interdisciplinary Reviews: Developmental Biology 3:445–463.https://doi.org/10.1002/wdev.151

Cytonemes as specialized signaling filopodiaDevelopment 141:729–736.https://doi.org/10.1242/dev.086223

Do morphogen gradients arise by diffusion?Developmental Cell 2:785–796.https://doi.org/10.1016/S15345807(02)00179X

Nodal and BMP dispersal during early zebrafish developmentDevelopmental Biology 447:14–23.https://doi.org/10.1016/j.ydbio.2018.04.002

Morphogen gradients: from generation to interpretationAnnual Review of Cell and Developmental Biology 27:377–407.https://doi.org/10.1146/annurevcellbio092910154148

Mathematical models of morphogen gradients and their effects on gene expressionWiley Interdisciplinary Reviews: Developmental Biology 1:715–730.https://doi.org/10.1002/wdev.55

Role of cytonemes in Wnt transportJournal of Cell Science 129:665–672.https://doi.org/10.1242/jcs.182469

New model for understanding mechanisms of biological signaling: direct transport via cytonemesThe Journal of Physical Chemistry Letters 7:180–185.https://doi.org/10.1021/acs.jpclett.5b02703

Mechanisms of the formation of biological signaling profilesJournal of Physics A: Mathematical and Theoretical 49:483001.https://doi.org/10.1088/17518113/49/48/483001

Fundamental limits to position determination by concentration gradientsPLOS Computational Biology 3:e78.https://doi.org/10.1371/journal.pcbi.0030078

Emergent versus IndividualBased multicellular chemotaxisPhysical Review Letters 119:188101.https://doi.org/10.1103/PhysRevLett.119.188101

Control of signaling molecule range during developmental patterningCellular and Molecular Life Sciences 74:1937–1956.https://doi.org/10.1007/s0001801624335
Decision letter

Raymond E GoldsteinReviewing Editor; University of Cambridge, United Kingdom

Marianne E BronnerSenior Editor; California Institute of Technology, United States
In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
Acceptance summary:
During embryonic development, morphogen gradients provide positional information to differentiating cells. Experiments have demonstrated that the precision by which these gradients are created and read out is often very high, raising the question of the fundamental limits by which nuclei and cells can obtain positional information.
Fancher and Mugler compare two mechanisms for gradient formation, directtransport and synthesisdiffusionclearance, and show that the former is favoured for steep gradients and the latter at shallow gradients, in good qualitative agreement with observations.
Decision letter after peer review:
Thank you for submitting your article "Diffusion vs. direct transport in the precision of morphogen readout" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Marianne Bronner as the Senior Editor. The reviewers have opted to remain anonymous.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
We would like to draw your attention to changes in our revision policy that we have made in response to COVID19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.
Summary:
The authors present theoretical results concerning two canonical, alternative mechanisms for patterning of cells by morphogen gradients. The issue is that cells know about their positions in the organism, and thus their intended cell fates, from measurements of morphogen concentrations. There will necessarily be uncertainty due noise in this concentration, and evolution has presumably favored mechanisms that reduce this noise (in particular relative to signal – namely the average difference in morphogen levels between adjacent cells). The first mechanism is direct transport (DT) of morphogen from a source cell to target cells via specific channels (e.g. cytonemes). The second mechanism is simple diffusion with degradation, which they call synthesisdiffusionclearance (SDC). By rigorously formulating noise models for these different schemes, the authors draw several interesting conclusions. First, the transport process associated with DT does not in itself contribute to the noise. This follows from the assumption that morphogen molecules are independent so that the details of transport only yield a the net rate of arrival of molecules at the target cell. More importantly, the authors discover that there is a crossover as a function of profile length from DT as the preferred mechanism to SDC as the preferred mechanism. The authors do an excellent job of presenting intuitive arguments for this and other results. (In this case, the essential disadvantage of the DT mechanism is that cells can only glean information from the morphogens that they absorb, whereas in the SDC mechanism cells can sense all molecules that pass by them.) The authors follow up this theoretical study with an analysis of multiple developmental morphogen systems. The results are impressively consistent with their theoretical predictions concerning which mechanism has higher signaltonoise given the profile lengths. Overall, this is an exemplary paper: it identifies an important unrecognized question, rigorously formulates and solves theoretical models to address the question, and carefully applies the results to gain original insights into wellstudied developmental systems.
Essential revisions:
1) We find the notation in the paper is unnecessarily complicated. For example, all the overbars in Equation 1 are not needed if the quantities are defined to be the mean. Otherwise, we have symbols with three labels, the bar, the j and +/. Notation in a biology journal must be very carefully chosen for maximum simplicity and clarity. Likewise, the δ functions in these equations could more clearly be shifted to statements about boundary conditions, which would be more intuitive. Remember, very few biologists understand δ functions!
2) The argument presented in the third paragraph of the subsection “Direct Transport Model” is a key one, but we find that it could be explained more clearly. Perhaps by reference to a more detailed diagram?
3) The lack of parallel construction between the presentations of the two models is odd. The DT model is presented as a set of deterministic ODEs for mean concentrations, while the diffusive model (itself an averaged equation) has noise terms added. We would have expected the statement about the noise effect in the DT model to be deduced from some underlying master equation of which the presented model is simply the first moment(s), but that higher order moments are necessary to calculate to draw conclusions about the model.
4) Stepping back from the calculations we are struck by the following question. If, as stated: "We see that regardless of the form of p(τ), the probability of a morphogen molecule entering the target cell in any given small time window δt is simply βδt. This result holds regardless of the mechanism by which morphogen molecules go
from the source cell to the target cell, as the only effect such a mechanism can have is on p(τ)", then why can't we substitute a diffusive mechanism and deduce the same thing?
5) The authors assume that once a morphogen molecule has reached the end of the cytoneme, it is immediately absorbed by the target cell. How realistic is this assumption and how important is it? Would relaxing it change the results qualitatively?
6) In comparing the precision of the two models, it would be good to discuss in a bit more detail how the comparison is made: which parameters are kept the same, which are optimized over, which are varied systematically, and why is this choice the most natural one? For example, the authors chose to study the precision ratio as a function of the gradient length, while optimizing over the DT shape φ. Βut we could also imagine a comparison in which the production and degradation rates are kept the same for both models (because production and degradation are energetically costly), but that over all other parameters the precision is optimized. Would that give a qualitatively different result? Why would the gradient range λ be of special importance (the comparison is made on the footing of equal gradient range)? Should not only the precision matter (such that it would be fine that the gradients in the DT and SDC model are different)? In other words, how do the maximal precisions that can be reached given reasonable constraints (such as production and degradation rates) compare, as a function of the distance to the source?
7) And in making this comparison, does the SDC model exhibit an optimal diffusion constant that maximizes the precision? Understanding this is probably important, because when, in the current comparison, the SDC model is more precise, it is because of the higher protein clearance rate due to diffusion. Yet, a higher diffusion constant also lowers the steepness of the gradient, decreasing the precision,
8) The key mechanism by which the SDC model can become superior is indeed diffusion. Could adding diffusion to the DT model improve the performance of the latter model? Is there evidence for diffusion in experimental systems that employ directed transport?
9) In DT systems, is it clear that molecule transport is independent? How could correlated transport be included in the DT model?
10) Also, in the DT model could noise be reduced by cells measuring arrivals of particles rather than averaging over internal concentration of particles? This would presumably only change the resulting expression for precision by a numerical factor, but could this factor shift the balance in favor of DT in some cases?
https://doi.org/10.7554/eLife.58981.sa1Author response
Essential revisions:
1) We find the notation in the paper is unnecessarily complicated. For example, all the overbars in Equation 1 are not needed if the quantities are defined to be the mean. Otherwise, we have symbols with three labels, the bar, the j and +/. Notation in a biology journal must be very carefully chosen for maximum simplicity and clarity. Likewise, the δ functions in these equations could more clearly be shifted to statements about boundary conditions, which would be more intuitive. Remember, very few biologists understand δ functions!
We have made the suggested revisions to the notation by removing the bar notation entirely. Instead, we express stochastic variables as time dependent and their steady state means as time independent. We have also removed the δ functions from the dynamic equations for the DT model (Equation 1) and expressed them instead as boundary conditions. We have left the δ functions in the equations for the SDC model though, as our method of incorporating Langevin noise terms requires the use of δ functions rather than simple boundary conditions. Finally, we have changed all + and − labels to be superscripts so as to make the notation more consistent across variables.
2) The argument presented in the third paragraph of the subsection “Direct Transport Model” is a key one, but we find that it could be explained more clearly. Perhaps by reference to a more detailed diagram?
We have added more text to better clarify the necessary assumptions needed to make this argument as well as created a new figure (Figure 2) to provide a visualization.
3) The lack of parallel construction between the presentations of the two models is odd. The DT model is presented as a set of deterministic ODEs for mean concentrations, while the diffusive model (itself an averaged equation) has noise terms added. We would have expected the statement about the noise effect in the DT model to be deduced from some underlying master equation of which the presented model is simply the first moment(s), but that higher order moments are necessary to calculate to draw conclusions about the model.
We have added a paragraph just before the DT section of our results to address this potential source of confusion. Specifically, our method in both models is to solve for certain moments of the master equation. In the DT model, we only require the first moment to fully characterize the statistical properties of the morphogen molecule count in the target cells, while in the SDC model we solve for the second moment via the known properties of the noise terms introduced into the equation for the first moment.
4) Stepping back from the calculations we are struck by the following question. If, as stated: "We see that regardless of the form of p(τ), the probability of a morphogen molecule entering the target cell in any given small time window δt is simply βδt. This result holds regardless of the mechanism by which morphogen molecules go
from the source cell to the target cell, as the only effect such a mechanism can have is on p(τ)", then why can't we substitute a diffusive mechanism and deduce the same thing?
Our SDC model violates a key assumption of the argument depicted in the new Figure 2 by allowing molecules to leave the target cell after entering it. We have added additional text both in the description of the argument itself as well as the SDC section of our results to reinforce this.
5) The authors assume that once a morphogen molecule has reached the end of the cytoneme, it is immediately absorbed by the target cell. How realistic is this assumption and how important is it? Would relaxing it change the results qualitatively?
While it is difficult to speak to the realism of this assumption, as we are unaware of any experimental study that investigates the dynamics of molecules specifically at the ends of cytonemes, we can say that it is not particularly important. Relaxing this assumption would only alter the specifics of the transport mechanism, which as we argue with our new Figure 2, does not change the noise properties.
The molecule count in the target cells in the DT model would still behave identically to a birthdeath system. The only way relaxing this assumption would qualitatively change our results would be if it caused a significant alteration to the mean profile.
6) In comparing the precision of the two models, it would be good to discuss in a bit more detail how the comparison is made: which parameters are kept the same, which are optimized over, which are varied systematically, and why is this choice the most natural one? For example, the authors chose to study the precision ratio as a function of the gradient length, while optimizing over the DT shape φ. Βut we could also imagine a comparison in which the production and degradation rates are kept the same for both models (because production and degradation are energetically costly), but that over all other parameters the precision is optimized. Would that give a qualitatively different result? Why would the gradient range λ be of special importance (the comparison is made on the footing of equal gradient range)? Should not only the precision matter (such that it would be fine that the gradients in the DT and SDC model are different)? In other words, how do the maximal precisions that can be reached given reasonable constraints (such as production and degradation rates) compare, as a function of the distance to the source?
We have significantly altered our description of how we compare the two models to better clarify our process here. Specifically, we now discuss in much greater detail which parameters are equated between the two models and why. The reviewers are correct to point out that the production and degradation rates (β and ν) should be equated due to their energetic restrictions. While we now present this as our rationale for equating β, we also discuss why the explicit value of ν is not relevant so long as holds true. Thus, while equating β and ν between models is consistent with our procedure, it still leaves the profile lengthscale λ^{ˆ} as a free parameter. Because the profile lengthscale is a frequently measured parameter across a variety of developmental systems, and our goal here is not only to understand the physical mechanisms behind developmental precision but also to quantitatively compare to data, we do not optimize over λ^{ˆ}. Instead we imagine that the profile lengthscale is set by a functional objective of the developing system, such as where a boundary is to form, and we ask, given this constraint, which model has the higher precision.
7) And in making this comparison, does the SDC model exhibit an optimal diffusion constant that maximizes the precision? Understanding this is probably important, because when, in the current comparison, the SDC model is more precise, it is because of the higher protein clearance rate due to diffusion. Yet, a higher diffusion constant also lowers the steepness of the gradient, decreasing the precision,
Indeed, there is an optimal value of the diffusion constant (and thus λ^{ˆ}) in the SDC model, for the precise reasons outlined by the referee. However, this optimal value depends on the choice of cell position: a smaller diffusion coefficient maximizes precision at nearby cells, whereas a larger diffusion coefficient maximizes precision at faraway cells. For the same reasons as outlined in the previous response, we do not presuppose the position at which the maximal precision is desired. Instead, we vary λ^{ˆ}, which allows us to compare the two models systematically and then compare the results to the experimental data.
8) The key mechanism by which the SDC model can become superior is indeed diffusion. Could adding diffusion to the DT model improve the performance of the latter model? Is there evidence for diffusion in experimental systems that employ directed transport?
We are unaware of any experimental study into a possible hybrid system that incorporates both diffusion and directed transport. Nonetheless, we have added a brief comment on such a possibility in our Discussion section.
9) In DT systems, is it clear that molecule transport is independent? How could correlated transport be included in the DT model?
To our knowledge, there have not been detailed experimental studies into the correlation between dynamics of individual morphogen molecules within cytonemes. Introducing correlated transport would violate a key assumption of our argument presented in our new Figure 2 and would create nonlinear and most likely unsolvable noise terms. As such, we consider the investigation into correlated transport to be beyond the scope of this particular work.
10) Also, in the DT model could noise be reduced by cells measuring arrivals of particles rather than averaging over internal concentration of particles? This would presumably only change the resulting expression for precision by a numerical factor, but could this factor shift the balance in favor of DT in some cases?
Measuring the arrival rate of molecules in the DT model would at best reduce the variance by a factor of 2, as the noise from degradation would be eliminated but the noise from arrival would remain. While this would increase the precision of the DT model, it would not do so significantly enough, and our results remain almost entirely unchanged by its inclusion. We have added a footnote in the manuscript to address this possibility.
https://doi.org/10.7554/eLife.58981.sa2Article and author information
Author details
Funding
Simons Foundation (376198)
 Sean Fancher
 Andrew Mugler
Simons Foundation (568888)
 Sean Fancher
National Science Foundation (PHY1945018)
 Andrew Mugler
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
This work was supported by Simons Foundation Grants No. 376198 and 568888 and National Science Foundation Grant No. PHY1945018. We thank Chris Bairnsfather for useful discussions.
Senior Editor
 Marianne E Bronner, California Institute of Technology, United States
Reviewing Editor
 Raymond E Goldstein, University of Cambridge, United Kingdom
Version history
 Received: May 15, 2020
 Accepted: October 13, 2020
 Accepted Manuscript published: October 14, 2020 (version 1)
 Version of Record published: November 4, 2020 (version 2)
 Version of Record updated: November 6, 2020 (version 3)
Copyright
© 2020, Fancher and Mugler
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics

 974
 Page views

 204
 Downloads

 5
 Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading

 Neuroscience
 Physics of Living Systems
Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odortaxis requires a detailed understanding of the animal’s sensory environment. For small model organisms like Caenorhabditis elegans and larval Drosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor’s adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations as they navigate spatial odor landscapes.

 Physics of Living Systems
 Structural Biology and Molecular Biophysics
Nonmembranebound biomolecular condensates have been proposed to represent an important mode of subcellular organization in diverse biological settings. However, the fundamental principles governing the spatial organization and dynamics of condensates at the atomistic level remain unclear. The Saccharomyces cerevisiae Lge1 protein is required for histone H2B ubiquitination and its Nterminal intrinsically disordered fragment (Lge1_{180}) undergoes robust phase separation. This study connects single and multichain allatom molecular dynamics simulations of Lge1_{180} with the in vitro behavior of Lge1_{180} condensates. Analysis of modeled proteinprotein interactions elucidates the key determinants of Lge1_{180} condensate formation and links configurational entropy, valency, and compactness of proteins inside the condensates. A newly derived analytical formalism, related to colloid fractal cluster formation, describes condensate architecture across length scales as a function of protein valency and compactness. In particular, the formalism provides an atomistically resolved model of Lge1_{180} condensates on the scale of hundreds of nanometers starting from individual protein conformers captured in simulations. The simulationderived fractal dimensions of condensates of Lge1_{180} and its mutants agree with their in vitro morphologies. The presented framework enables a multiscale description of biomolecular condensates and embeds their study in a wider context of colloid selforganization.