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The impact of experimental design choices on parameter inference for models of growing cell colonies

To better understand development, repair and disease progression, it is useful to quantify the behaviour of proliferative and motile cell populations as they grow and expand to fill their local environment. Inferring parameters associated with mechanistic models of cell colony growth using quantitative data collected from carefully designed experiments provides a natural means to elucidate the relative contributions of various processes to the growth of the colony. In this work, we explore how experimental design impacts our ability to infer parameters for simple models of the growth of proliferative and motile cell populations. We adopt a Bayesian approach, which allows us to characterize the uncertainty associated with estimates of the model parameters. Our results suggest that experimental designs that incorporate initial spatial heterogeneities in cell positions facilitate parameter inference without the requirement of cell tracking, while designs that involve uniform initial placement of cells require cell tracking for accurate parameter inference. As cell tracking is an experimental bottleneck in many studies of this type, our recommendations for experimental design provide for significant potential time and cost savings in the analysis of cell colony growth.

Introduction
The study of how cell populations grow and spread is integral to understanding and predicting the invasion of cancer, the speed of wound repair and the robustness of embryonic development [1][2][3]. However, the extent to which cell populations grow and spread is governed by multiple processes, including motility, proliferation, adhesion and cell death, making it difficult to elucidate the relative contributions of these processes to the growth and invasion of a cell colony [4]. As such, in vitro cell biology assays are routinely used to probe the mechanisms by which cells interact, and the key processes involved in the growth and expansion of cell colonies. These in vitro assays generally involve seeding a population of cells on a twodimensional substrate, and observing the population as the individual cells move and proliferate and the density of the monolayer increases towards confluence. A useful approach to interpret the results of these assays involves using a mathematical model that incorporates mechanistic descriptions of processes such as cell motility and proliferation. By parametrizing and validating the models using quantitative data from in vitro assays, it is possible to provide quantitative insights into the mechanisms driving the growth and spreading of a cell population, and make experimentally testable predictions. However, it is not always clear how best to choose the experimental design, nor which summary statistics of the data to collect, in order to accurately and efficiently parametrize and validate models.
In this work, we use a two-dimensional lattice-based exclusion process model that incorporates both motility and proliferation mechanisms. Our goal is to assess how our ability to accurately infer model parameters is affected by changes in the experimental design. Parameter inference is performed in a Bayesian framework using approximate Bayesian computation (ABC), allowing us to quantify the uncertainty of our parameter estimates and bypass the need to compute a likelihood function for the mechanistic model. By quantifying the information gain using the different experimental protocols, we are able to provide guidelines for experimental design in terms of the selection of experimental geometry and the collection of relevant quantitative summary statistics from imaging data.

Experimental design
Typically, there are two main types of two-dimensional in vitro experiments that are considered at the level of the population. The first experiment, shown in figure 1a, is often referred to as a growth-toconfluence assay. Here, we observe a population of cells seeded, initially at low density, as the cells move and proliferate and the population increases in number to eventually occupy the whole domain under observation [7,8]. The second experiment we consider is shown in figure 1b and is often referred to as a scratch assay. It involves perturbing a population of cells at, or near, confluence by scraping away a region of the population and observing the resulting spread of the population into this, now empty, region [9,10]. In this work, we will explore the extent to which models of these two types of assays can be parametrized using various summary statistics of these experimental data.

Approximate Bayesian computation and summary statistics
Parameter inference is approached generally in one of two ways, through either a frequentist approach or a Bayesian approach [11,12]. In frequentist inference, one generally seeks a point estimate of a parameter through maximum-likelihood estimation, and captures uncertainty in the estimate through the generation of confidence intervals. A Bayesian approach instead derives a predictive posterior distribution for the model parameters u given observed data D obs [13]. The posterior, P(u j D obs ), satisfies P(u j D obs ) / L(D obs j u) p(u), where L(D obs j u) is the likelihood of obtaining the data from the given model and parameters, and the prior, p(u), captures any previous knowledge of the parameters. For the mechanistic model we use in this work, the likelihood is intractable and so we use ABC to generate an approximate posterior distribution.
ABC has been used previously in the biological sciences, particularly to target complex problems in systems biology [14,15], population genetics [16] and ecology [17]. There is also growing popularity in the use of ABC specifically in the investigation of cell biology processes [18 -20]. ABC relies on repeated simulation of the model using parameters from the prior distribution, and the acceptance of these parameter sets whenever the model output is sufficiently close to the experimental data. These accepted parameter sets are then used to estimate the posterior distribution. In using ABC in practice, there are several important user choices, most importantly the means of comparison between simulated and experimental data, typically through summary statistics and a suitable distance metric, and the threshold used for accepting or rejecting parameter sets [21,22]. In particular, the summary statistics represent lower dimensional descriptions of the data and their choice is vitally important as they impact the amount of information gained about model parameters through the use of ABC [23].
The major new insights provided in this work are a quantitative understanding of how both the choices of experimental geometry and summary statistics impact the quality of the posteriors generated using ABC. The use of ABC rejection allows us to compare the quality of the posteriors resulting from a wide range of possible summary statistics in a computationally efficient way, and we quantify the information gain using the Kullback -Leibler divergence [24]. We also demonstrate how data-cloning ABC (ABC-DC) [25] can potentially be used to obtain maximum-likelihood estimates (MLEs) of model parameters more efficiently than ABC rejection. Instead of targeting the likelihood of the observed data, the data cloning approach targets the likelihood corresponding to a large number of copies (also known as clones) of the data, where each data clone is assumed independent of the others. Data cloning results in a posterior distribution that has the MLE as its mean, and the variance can be related to the asymptotic variance of the MLE [26]. ABC-DC uses ABC Markov chain Monte Carlo with data cloning to facilitate convergence towards a MLE [25].

Aims and outline
The aim of this work is to demonstrate the use of Bayesian inference methods to characterize the motile and proliferative behaviour of individual cells within growing cell colonies. In particular, we aim to estimate parameters of a lattice-based volume exclusion model for cell colony growth in a variety of experimental geometries. In addition, we demonstrate the use of ABC-DC for more efficient maximum-likelihood estimation. In §2, we introduce our model, and the various ABC algorithms and summary statistics we employ in this work. In §3, we assess how experimental design choices impact the quality of estimated posterior distributions, using both ABC rejection and ABC-DC, and we conclude with a discussion of our results in §4. To confirm the accuracy of our inference process, and the relevance of the mechanistic model to the experimental data, we conclude by sampling from the posterior distributions and exploring whether key summary statistics predicted by the parametrized model, such as the size of the population and the displacement of individual cells, reflect our experimental observations within some reasonable confidence interval.  Figure 1. Two experimental designs considered in this work. (a) Images from a growth-to-confluence assay using MDA MB 231 breast cancer cells. See [5] for more information. (b) Images from a scratch assay with PC3 prostate cancer cells. See [6] for more information. (c) Schematics of the model. We use an on-lattice model, as described in the text, and vary the initial condition to replicate the experiments in (a,b): we seed cells uniformly at random over 24 rows (left), 12 rows (middle) or six rows (right). The images in (a) are reproduced from Simpson et al. [5] with kind permission, whereas the images in (b) are reproduced from Johnston et al.

Mechanistic model
We employ a simple two-dimensional lattice-based exclusion process model akin to that of Simpson et al. [27], whereby N(t) cells occupy a square lattice with R rows and C columns at time t. During each time step of duration t, we choose N(t) cells at random with replacement to attempt a movement and proliferation event into orthogonally adjacent lattice sites with probabilities P m and P p , respectively. For each cell, we draw a uniform random number, r U(0, 1). If r P m , the cell attempts a movement event into one of the four orthogonally adjacent lattice sites with equal probability, and if P m , r P m þ P p the cell attempts a proliferation event, whereby a daughter cell is placed into one of these lattice sites, each with equal probability. If P m þ P p , r 1, no movement or proliferation event is attempted. If a cell attempts to move or to place a daughter cell into an occupied lattice site, or outside of the domain, the attempted movement or proliferation event is aborted. These parameters in the discrete model are related to the classical diffusion coefficient, D, and cell proliferation rate, l, by D ¼ lim D! 0,t ! 0 P m D 2 /(4t) and l ¼ lim D! 0,t ! 0 P p /t [28].
To replicate experimental images, we take R ¼ 24, C ¼ 32, where lattice sites have length D ¼ 18.75 mm (corresponding to the approximate cell diameter of the cells considered in typical experiments). Simulations are initialized with cell positions randomly distributed in the firstR rows of the domain, whereR is chosen to mimic potential experimental conditions. To interpolate between the growth-toconfluence and scratch assay designs, we choose three initial conditions (figure 1c) initializing cells uniformly at random across either 24, 12 or six rows of the domain. We note that, in reality, the size of the experimental domain is much larger than that captured in the experimental images [6]. To confirm that our results are insensitive to this choice of domain size, we repeated our investigations with simulations carried out on a larger domains and data collected on a subset of this domain that corresponds to the experimental image size. Results are shown in electronic supplementary material, figure S6.

In silico data
As our aim in this work is to better understand how experimental design impacts our ability to infer model parameters, we use our mechanistic model to generate in silico (observed) data that closely replicate that available from experiments (figure 1a,b) and attempt to infer the parameters used to generate these in silico data. We use a time step of t ¼ 1/24 h, and model parameters P m ¼ 0.25 and P p ¼ 0.0025 (motivated by estimates for the diffusivity and proliferation rates from similar experiments [6]). We record the final positions of cells in our in silico experiments after T ¼ 12 h, equating to 288 time steps. We also record trajectory data for five randomly chosen cells by recording their positions every eight time steps (corresponding to every 20 min [5]). As experiments are typically repeated several times to ensure reproducibility of results [5], we repeat simulations M ¼ 10 times and average the resulting statistics. To confirm the consistency of our results for larger values of the final simulation time, T, we repeat our methodology for longer periods of time, either T ¼ 24 h or T ¼ 36 h. Results are shown in the electronic supplementary material, figure S5.

Inference
We use ABC to estimate posterior distributions for the model parameters, u ¼ (P m , P p ). ABC rejection is performed by repeatedly sampling from the prior distribution, p(u), simulating the model, and accepting parameters that result in simulation output sufficiently close to the observed data. The accepted parameters are used to compute the approximate posterior distribution, P(u j D obs ). In order to assess how close the simulated and observed data are, we consider a range of summary statistics (detailed in §2.3.2) and an appropriate distance function (described in §2.3.3).
In order to quantify the performance of different summary statistics in inferring model parameters, we choose a uniform (uninformative) prior,

. Approximate Bayesian computation rejection
Algorithm 1 describes the ABC rejection algorithm we use in this work. In algorithm 1, we avoid directly specifying an acceptance threshold, e, by accepting the 1st percentile of the samples (ranked in terms of the distance between the simulated and observed data) and taking the number of samples, K, sufficiently large to obtain an accurate approximation to the true posterior. The accepted samples are used to estimate a posterior distribution using bivariate kernel density estimation with a Gaussian kernel [29].
simulate D k from the model with parameter u k 4: compute summary statistics, s k j (l ), for j ¼ 1, . . . , 13

Summary statistics
In order to compare observed and simulated data, we reduce the dimension of the data using summary statistics. How best to choose summary statistics for parameter inference is an ongoing research question, and some automated procedures have been developed for this choice, typically through either minimizing a loss of information function [30], or maximizing the gain of information [23] through a measure such as the Kullback -Leibler divergence [24].
Kulback-Leibler divergence. The Kullback -Leibler divergence is a measure of the relative difference in two continuous distributions, F and G, and defined as In this work, we generate bivariate posteriors and priors which are each functions of u ¼ (P m , P p ). We discretize these distributions onto a fine mesh, 512 Â 512 in size, for plotting. We compute the Kullback -Leibler divergence, I KL , by numerically integrating over both dimensions. The observed data consist of the positions of all cells at the terminal time of the assay together with the tracks of five randomly chosen cells, i ¼ 1, . . . , 5. We let N(t n ) be the number of cells at time t n ¼ t Â n, i.e. at the nth iteration of a simulation, so that n ¼ 288 corresponds to the final simulation time, k is the horizontal distance between cell i and cell j at time t n , and similarly for dY ij . We summarize these data using statistics motivated by random walk models [5,20,23], considering thirteen statistics in total, labelled as in table 1.
-The first summary statistic we consider is the final cell number, N(T ).
-In order to assess whether cells are forming clusters, the second and third summary statistics we consider relate to the size of the largest cluster of cells, computed using the Matlab function bwconncomp. 1 We consider either 4-connected clusters (cells orthogonally adjacent are part of a single cluster), k 4 (T ), or 8-connected clusters (cells diagonally adjacent are also part of a single cluster), k 8 (T ). -Summary statistics four to six correspond to the binning variance [5], which quantifies the deviation from the average number of cells expected in quadrats of the domain, and is computed as where n(b, T ) is the number of cells in bin b at time T, and B k the number of bins when the bin width is k. -As summary statistics seven and eight, we consider trajectory statistics from the five tracked cells (labelled i ¼ 1, . . . , 5): either the total Manhattan displacement (the sum of horizontal and vertical distances moved) of the cells, kxk, where or the tortuosity of the trajectory, G, where -Summary statistic nine is the smallest eigenvalue, l, of the gyration tensor [31], and  Table 1. A list of the summary statistics and the corresponding notation adopted. Note, apart from cell trajectory statistics, all summary statistics are evaluated at the final time, T.
summary statistic notation rsos.royalsocietypublishing.org R. Soc. open sci. 5: 180384 cells, C Y (due to the heterogeneity of initial condition in the y direction), or the total separation of cells, and 1 flg is the indicator function which, in this case, counts pairs of cells separated by lattice distance l. -Finally, we consider the correlation functions normalized by the expected number of pairs at each distance l ¼ 1, . . . , L, which accounts for the density of cells in the domain, and C and R are the numbers of columns and rows in the lattice, respectively.

Computing distances
Experiments are typically performed multiple times in similar conditions, so we simulate multiple datasets, and call each set a replicate. The observed data, D obs , consist of M replicates, The simulated data, D, consist of M replicates and K samples, From the simulated data, we compute each summary statistic, s j (l ), where j ¼ 1, . . . , 13 denotes the 13 different summary statistics, and l ¼ 1, . . . , L j is the lth element of statistic (L j ¼ 24 for the pairwise correlation function statistics because there are 24 rows in the lattice, and L j ¼ 1 otherwise).
First we average over the replicates, then we compute the median absolute deviation (MAD) for each statistic, s j (l ). This is defined for a univariate dataset, X ¼ fX i g, as MAD ¼ median(jX i 2 median(X )j) . We then compute the distances for each statistic, j ¼ 1, . . . , 13, as Note that this choice of distance function weights vectors of summary statistics of different lengths equally. This is of particular importance when performing parameter inference using combinations of summary statistics, as in this work. We consider combinations of A ¼ 1, 2 or 3 summary statistics from the 13 statistics listed in table 1 to give

Regression adjustment
After performing ABC rejection (and during ABC-DC), we perform regression adjustment to derive a more accurate estimate of the posterior distribution [16,25,32]. We assume that u satisfies the following regression model: where I ¼ fk : d k , dg, B ¼ jIj, s denotes the vector of summary statistics considered in the distance function (2.15) and the j k are uncorrelated Gaussian random variables with zero mean and common variance s 2 . The regression model is solved to find the least-squares estimates, (â,b), and the accepted parameter sets are adjusted according to

Data-cloning approximate Bayesian computation
Data-cloning ABC involves considering a dataset, D obs K , containing K clones of the experimental data, D obs , that is, D obs K ¼ (D obs , D obs , . . . , D obs ), where each clone is assumed independent of the others. The likelihood of the cloned data is then [25,26] L(D obs K j u) ¼ (L(D obs j u)) K : ð2:19Þ Hence, the posterior distribution resulting from cloned data satisfies and the MLE of u is equivalent to the mean of P(u j D obs K ) as K ! 1 [26]. Intuitively, we can see this by reasoning that, if all of the independent model-generated datasets are close to the experimental data, we are K times as likely to have selected a sensible candidate parameter.
The ABC-DC algorithm was proposed by Picchini et al. [25], and it uses ABC Markov chain Monte Carlo [33]. The algorithm works in two stages first the acceptance threshold, e, is decreased according to a tolerance scheme fe 1 , e 2 , . . . , e P g, and then the number of clones, K, is increased according to a clone scheme fK Pþ1 , K Pþ2 , . . . , K PþQ g, where subscripts index the population number. The MLE is approximated by averaging the final parameter population, and the 90% credible interval can be used for comparison to ABC rejection.
For each sample, a proposal u # is accepted or rejected according to the acceptance probability [25] J e j (D # , D obs ) ¼ exp À which approximates a Gaussian centred on the experimental observation with variance e, weighted according to the MAD, s j (l ), of the summary statistic j under consideration, where the MAD is estimated using ABC rejection. The ABC-DC method is described in full in algorithm 2. The number of samples, r, for the decreasing tolerance stage is denoted by fr 1 , . . . , r P g, and fr Pþ1 , . . . , r PþQ g denotes the number of samples for the increasing clones stage. The scheme we use has P ¼ 5 and Q ¼ 4, and other variables are as follows: number of samples, r ¼ (1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 2000), number of clones, K ¼ (1, 1, 1, 1, 1, 1, 2, 3, 4), value of tolerance, e ¼ 1: initialize u* ¼ (0.5, 0.005) 2: for p ¼ 1 to P do 3: set u 1 ¼ u*, and fix 1 ¼ 1 p , K ¼ 1 4: simulate D Ã from the model with parameter u*, and compute q Ã ¼ J e j (D Ã , D obs ) 5: for k ¼ 1 to r p 2 1 do 6: sample a new u # N(u Ã , (2:38) 2 S p =2), where S p is the covariance of the previous u population (or the prior if p ¼ 1) a . 7: simulate D # from the model with parameter u # , and compute q # ¼ J e j (D # , D obs ) 8: if v . a then 10: set u kþ1 : ¼ u k 11: end if 14: if p ¼ P and max p P , q # p(u # ) then 15: update current maximum-likelihood estimate: set max p1 P ¼ q # p(u # ) and u MLE ¼ u # . 16: end if 17: end for 18: end for 19: perform regression adjustment on the final u population, set S P as the covariance of the adjusted u. 20: for q ¼ 1 to Q do 21: set u 1 ¼ u*, and fix 1 ¼ 1 P , K ¼ K Pþq 22: simulate K independent data sets from the model, D Ã(1) , . . . ,D Ã(K) , with parameter u*, and compute q Ã ¼ Q K k¼1 J e j (D Ã(k) ,D obs ) 23: for k ¼ 1 to r Pþq 2 1 do 24: sample a new u # N(u MLE , S PþqÀ1 ) 25: simulate K independent data sets from the model, D #(1) , . . . , D #(K) , with parameter u*, and compute q

Results
Our aim in this work is to provide insights into how both the choices of experimental geometry and summary statistics impact the quality of the posteriors generated using ABC for experiments that are typically used to characterize growing and spreading cell populations. We generate in silico (observed) data using the model outlined in § §2.1 and 2.2 for the range of experimental designs illustrated in figure 1c, and quantify the quality of the posteriors resulting from the use of ABC rejection with various summary statistics using the Kullback -Leibler divergence between the prior and posterior distributions (details of the methods used can be found in §2.3). In § §3.1 and 3.2, we ask whether it is possible to quantify, using only a single summary statistic of the data, the relative contributions of proliferation and motility in a number of cell spreading experiments. In § §3.3, we explore the extent to which parameter estimates can be improved using multiple summary statistics. Finally, in §3.4 we demonstrate the use of ABC-DC to efficiently find MLEs, and compare these estimates to those generated using ABC rejection. 3.1. Can we infer both model parameters using a single summary statistic?
We first ask whether we can jointly infer both model parameters, P m and P p , using a single summary statistic in the distance function (2.15). To do this, we perform ABC rejection on in silico (observed) data gathered from simulations with P m ¼ 0.25 and P p ¼ 0.0025, where we replicate different experimental designs by varying the model initial conditions: we initialize the model with 24 cells in all 24 rows, or in 12 rows, or in six rows, of the domain, as shown in figure 1c. Note that this means we change the average density within the region into which cells are initialized, but that the overall cell number, and hence the average total density, is constant. Figure 2a-f shows the posteriors generated for selected summary statistics of the data. The average information gain, and its deviation, for each of the three experimental designs and all summary statistics, is summarized in figure 2g. Figure 2a,b shows that for a growth-to-confluence assay design (where cells are initialized across all 24 rows of the domain), an accurate estimate of P p can be obtained using the number of cells, N, as a summary statistic, and an accurate estimate of P m can be obtained using the Manhattan displacement, kxk, as a summary statistic. However, no single summary statistic can be used to infer both P p and P m with any degree of confidence. Figure 2c,d, however, demonstrates that, in contrast to a growth-toconfluence assay design, using a scratch assay design (where cells are initialized only in six rows of the domain) enables both P m and P p to be accurately estimated using a single summary statistic, for example the one-dimensional correlation function summary statistic, C Y . Finally, figure 2e,f demonstrates how the predicted posterior can vary for a single summary statistic as the experimental design is changed. We use as an example the binning variance statistic, Q 8 , which is the deviation in cell numbers from the mean when the domain is divided into 8 Â 8 bins. Increasing the rate of movement, P m , means clusters are broken up more rapidly, while increasing the rate of proliferation, P p , makes clusters larger. As a result, in silico (observed) data generated using small P m and P p result in the same binning variance as data generated with large P m and P p . The effect is more pronounced for a scratch assay design due to the initial confinement of cells. Figure 2g summarizes the quality of the predicted posteriors resulting from the use of each of the summary statistics listed in table 1 for each experimental design considered in figure 1c. Our results demonstrate that statistics based on cell numbers provide the same or less information as cells are increasingly confined initially, whereas the opposite is true for the summary statistics that relate to correlations in cell positions. Both model parameters, P m and P p , can be inferred using a single summary statistic, without the need for cell trajectory information, but only when cells are initially confined to a scratch-assay-like geometry. For growth-to-confluence assays, cell trajectory data are necessary to accurately infer both model parameters.

What is the impact of the initial number of cells on the posteriors?
Next, we consider how changing the initial number of cells in the assay (24,48 or 72 cells) affects the quality of the predicted posterior distribution. We consider either a growth-to-confluence assay (cells initialized uniformly at random across the domain, figure 3a -c) or a scratch assay design (cells initialized uniformly into six rows of the domain, figure 3d-f ). Figure 3a -c demonstrates that, for the growth-to-confluence assay, increasing the initial cell number does not facilitate accurate inference of both model parameters using a single summary statistic. In line with the results presented in figure 2, figure 3d-f indicates that both model parameters can be accurately estimated using a single summary statistic when the scratch assay is used; however, increasing the initial cell number does not significantly increase the quality of the posterior. We quantify the quality of the estimated posterior for each summary statistic and each experimental design in the bar plots in figure 3c,f. For almost every summary statistic, the quality of the posterior increases as the initial cell number increases, likely due to a reduction in the variance of each summary statistic.

Are trajectory statistics necessary for maximal information gain?
It is natural to ask whether multiple summary statistics can be combined to increase the quality of the estimated posteriors and, further, whether combining different summary statistics can avoid the need to collect cell trajectory data to accurately infer parameters of a growth-to-confluence assay. To answer this question, we considered all combinations of two or three summary statistics from the 13 under consideration, computing the resulting posteriors and information gain for each combination. Figure 4 demonstrates that, for a growth-to-confluence assay, cell trajectory data are necessary to generate a rsos.royalsocietypublishing.org R. Soc. open sci. 5: 180384     posterior that allows for accurate estimation of both parameters P m and P p (figure 4a,b). In particular, trajectory information is necessary to estimate the motility parameter, P m . It is interesting to note that, in both cases (with and without trajectory information), the number of cells summary statistic, N, is  included in the best-performing two-summary-statistic combinations, likely because it facilitates accurate estimation of the proliferation parameter, P p . For a scratch assay, cell trajectory information is not necessary to accurately infer both model parameters; however, as expected, including cell trajectory data enables more accurate inference of the cell motility parameter, P m . Figure 4e quantities the quality of the estimated posteriors for a range of experimental designs, initial cell densities and for combinations of one, two or three summary statistics. Results demonstrate that little extra information is gained by considering more than two summary statistics and that, as expected, accurate parameter inference for growth-to-confluence assays requires cell trajectory data.

Can data-cloning approximate Bayesian computation provide an efficient means to estimate model parameters?
Finally, we investigate whether the ABC-DC algorithm proposed by Picchini et al. [25] can provide an efficient means to estimate model parameters. ABC-DC essentially involves using a number of copies of the observed data (called 'clones') that are assumed to be independently generated datasets. ABC-DC uses ABC Markov chain Monte Carlo [33] over two stages: first the acceptance threshold, e, is decreased; and then the number of clones, K, is increased. This has the impact of concentrating the posterior around the MLE, with the posterior mean an approximation to the MLE, and the 90% credible interval available for quantification of the uncertainty in parameter predictions. Figure 5 shows the results of using ABC-DC with the two-dimensional pairwise correlation function, C XY , to estimate posterior distributions for both P m and P p . Figure 5a,c shows how the posterior changes as the number of clones is increased, relative to ABC rejection, while figure 5b,d shows the Markov chains of both P m and P p . Our results indicate that, as expected, the ABC-DC algorithm results in a tighter posterior focused approximately about the MLEs found using ABC-DC with a single instance of the data, K ¼ 1. To understand how the MLEs generated using ABC-DC differ from those generated using ABC rejection, we look at the posteriors generated using each algorithm together with three summary statistics: the number of cells, N; the two-dimensional pairwise correlation

Predictions for experimental data
We can use our model to infer parameter distributions for different cell types in a growth-to-confluence assay, and subsequently make predictions about long-time behaviours, such as the time taken to reach confluence. In figure 6, we use data from the growth-to-confluence experiments detailed in [5] to infer motility and proliferation rates for two different cell types: 3T3 fibroblast cells and MDA MB 231 breast cancer cells. In particular, for each cell type, we use 10 experimental replicates to estimate the bivariate posterior distribution for P m and P p , with data collected 12 h into the experiment. Figure 6a To check the validity of our model and inferred posterior distributions, we collected data from the same experimental replicates at 24 h and used these to perform a posterior predictive check. In figure  6cf, we present the results of performing the posterior predictive check using both the cell number, N, and the Manhattan displacement, kxk, as summary statistics. We produce results using, for each dataset, 1000 samples from the estimated posteriors, and visually compare the statistics for each experimental replicate with the average of the predicted statistics and their 95% confidence intervals. These plots show strong support for the model for each cell type. We see that for the number of cells statistic, all of the experimental statistics fall within the 95% confidence intervals. For the Manhattan displacement statistic, two of the 10 replicates for the 231 cells have an experimental statistic that falls outside the 95% confidence interval of the simulated statistic. In this case, we suspect translation of the positions of slow-moving cells (as in the case of the 231 cells) onto a lattice has introduced artefacts, causing discrepancy between simulation displacement and experimental displacement.

Discussion
Mechanistic models together with experimental data from cell biology assays provide an excellent opportunity to characterize the extent to which cell proliferation and cell motility can drive the growth and expansion of a cell colony. To be able to draw quantitative distinctions between the relative contributions of cell proliferation and motility for different cell types and biological scenarios requires the accurate inference of model parameters using quantitative summaries of experimental data.
In this work, we have applied ABC to explore the extent to which different experimental designs and quantitative summary statistics impact our ability to accurately infer parameters of a simple mechanistic Table 2. ABC-DC for a scratch assay experiment. The MLEs and the 5% -95% empirical percentiles are shown for parameters accepted using ABC rejection and ABC-DC, for different summary statistics. The speed up of the ABC-DC method over ABC rejection is also given. model of cell growth and spreading. The model we present is the simplest possible individual-based model of a two-dimensional cell biology experiment in which we can simulate cell motility and cell proliferation with a simple mechanism for contact inhibition. Our rationale for working with this fundamental model is that it is crucial to explore whether it is possible to parametrize this simple model using routinely collected experimental data, before attempting inference for a more biologically realistic model with a greater number of unknown parameters. Our results suggest that, for growth-to-confluence experiments, cell trajectory information, in addition to summary statistics such as cell numbers and spatial correlations, are required for accurate parameter inference. On the other hand, for a scratch assay geometry, it is possible to accurately infer both motility and proliferation parameters without recourse to tracking individual cells over multiple frames of a microscopy video. As cell tracking provides the bottleneck in the analysis of many experimental studies, this renders experimental geometry an important aspect to consider in the design of experiments. In addition, our results demonstrate that increases in the initial cell number in cell biology assays can increase the quality of estimated posterior distributions by decreasing noise in the summary statistics, a result also found for a similar model by Ross and co-workers [18]. We also demonstrated that posteriors of significantly increased quality can be generated using a combination of two summary statistics, but that further increases in the number of summary statistics provided little increase in the quality of the posteriors. In electronic supplementary material, figures S1 -S4, we demonstrate that our results are consistent across a range of parameter values and a range of experimental durations. However, it should be noted that, because we collected data at only the final time point of the experiment, some summary statistics lose efficacy if experiments are run over a sufficiently long time period that the population grows to confluence. For example, in this case, the number of cells, N, at the final time point provides little information on the model parameters. Finally, we have shown that the ABC-DC approach provides an efficient means to approximate the MLEs of model parameters, with significant improvements in computational efficiency provided where the summary statistics are time-consuming to calculate.

Conclusion
In summary, our results suggest that experimental design choices that incorporate initial spatial heterogeneities in cell positions facilitate parameter inference without the requirement of cell tracking, while those that seed cells uniformly initially require cell tracking for accurate parameter inference. As cell tracking is often a major technical limitation of many experimental studies of this type, our recommendations for experimental design choice could lead to significant potential time and cost savings in the analysis of these kinds of commonly used experiments.
Data accessibility. Codes used to generate the data are available from https://github.com/andrew-parker/Impact-ofexperimental-design.
wrote the manuscript.
Competing interests. We declare we have no competing interests.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Psychology

text:
High Throughput Measurement of Locomotor Sensitization to Volatilized Cocaine in Drosophila melanogaster

Drosophila melanogaster can be used to identify genes with novel functional roles in neuronal plasticity induced by repeated consumption of addictive drugs. Behavioral sensitization is a relatively simple behavioral output of plastic changes that occur in the brain after repeated exposures to drugs of abuse. The development of screening procedures for genes that control behavioral sensitization has stalled due to a lack of high-throughput behavioral tests that can be used in genetically tractable organism, such as Drosophila. We have developed a new behavioral test, FlyBong, which combines delivery of volatilized cocaine (vCOC) to individually housed flies with objective quantification of their locomotor activity. There are two main advantages of FlyBong: it is high-throughput and it allows for comparisons of locomotor activity of individual flies before and after single or multiple exposures. At the population level, exposure to vCOC leads to transient and concentration-dependent increase in locomotor activity, representing sensitivity to an acute dose. A second exposure leads to further increase in locomotion, representing locomotor sensitization. We validate FlyBong by showing that locomotor sensitization at either the population or individual level is absent in the mutants for circadian genes period (per), Clock (Clk), and cycle (cyc). The locomotor sensitization that is present in timeless (tim) and pigment dispersing factor (pdf) mutant flies is in large part not cocaine specific, but derived from increased sensitivity to warm air. Circadian genes are not only integral part of the neural mechanism that is required for development of locomotor sensitization, but in addition, they modulate the intensity of locomotor sensitization as a function of the time of day. Motor-activating effects of cocaine are sexually dimorphic and require a functional dopaminergic transporter. FlyBong is a new and improved method for inducing and measuring locomotor sensitization to cocaine in individual Drosophila. Because of its high-throughput nature, FlyBong can be used in genetic screens or in selection experiments aimed at the unbiased identification of functional genes involved in acute or chronic effects of volatilized psychoactive substances.

Drosophila melanogaster can be used to identify genes with novel functional roles in neuronal plasticity induced by repeated consumption of addictive drugs. Behavioral sensitization is a relatively simple behavioral output of plastic changes that occur in the brain after repeated exposures to drugs of abuse. The development of screening procedures for genes that control behavioral sensitization has stalled due to a lack of high-throughput behavioral tests that can be used in genetically tractable organism, such as Drosophila. We have developed a new behavioral test, FlyBong, which combines delivery of volatilized cocaine (vCOC) to individually housed flies with objective quantification of their locomotor activity. There are two main advantages of FlyBong: it is high-throughput and it allows for comparisons of locomotor activity of individual flies before and after single or multiple exposures. At the population level, exposure to vCOC leads to transient and concentration-dependent increase in locomotor activity, representing sensitivity to an acute dose. A second exposure leads to further increase in locomotion, representing locomotor sensitization. We validate FlyBong by showing that locomotor sensitization at either the population or individual level is absent in the mutants for circadian genes period (per), Clock (Clk,) and cycle (cyc). The locomotor sensitization that is present in timeless (tim) and pigment dispersing factor (pdf) mutant flies is in large part not cocaine specific, but derived from increased sensitivity to warm air. Circadian genes are not only integral part of the neural mechanism that is required for development of locomotor sensitization, but in addition, they modulate the intensity of locomotor sensitization as a function of the time of day. Motor-activating effects of cocaine are sexually dimorphic and require a functional dopaminergic transporter. FlyBong is a new and improved method for inducing and measuring locomotor sensitization to cocaine in individual Drosophila. Because of its high-throughput nature, FlyBong can be used in genetic screens or in selection experiments aimed at the unbiased identification of functional genes involved in acute or chronic effects of volatilized psychoactive substances.

INTRODUCTION
Behavioral sensitization is a relatively simple change in behavior, which is induced by repeated exposure to drugs of abuse. It is studied in laboratory animals as a behavioral endophenotype that aids in dissection of mechanisms of neuromodulation that are triggered by drug administration. It is primarily, but not exclusively, studied in relation to the motor-activating effects of the psychostimulants cocaine and methamphetamine (Steketee and Kalivas, 2011;Marinho et al., 2015). Sensitization following repeated administration of psychostimulants in rodents can be quantified as an increase in the amount of locomotion (locomotor sensitization), or an increase in the frequency or intensity of other, often stereotypical, motor behaviors such as rearing (behavioral sensitization).
The discovery that Drosophila melanogaster can develop behavioral sensitization to cocaine provided new genetic targets for testing of this phenomenon in vertebrate model organisms Hirsh, 1998, 1999). Behavioral sensitization in Drosophila is regulated by the circadian genes period (per), Clock (Clk), cycle (cyc), and doubletime (dbt), which provided at the time the first demonstration that circadian genes modulate druginduced plasticity (Andretic et al., 1999). Connection between a drug-induced behavior and the circadian system was further demonstrated by identification of Lmo (LIM-only) gene in a genetic screen for cocaine sensitivity in Drosophila (Tsai et al., 2004). Studies in rodents confirmed the importance of these genes in the regulation of multiple behavioral and cellular addiction-related phenotypes (Abarca et al., 2002;McClung et al., 2005;Lasek et al., 2010;Parekh et al., 2015).
Sensitization of Drosophila to cocaine has several direct parallels with behavioral sensitization in mammals, including: persistence over time, sexual dimorphism and conservation of both molecules and mechanisms (Andretic et al., 1999;McClung and Hirsh, 1999;Bainton et al., 2000;Park et al., 2000;Nestler, 2004;Chen et al., 2009). Dopaminergic signaling was shown to be important for the acute motor-activating effects of cocaine, ethanol and nicotine in Drosophila (Bainton et al., 2000;Kong et al., 2010), consistent with presynaptic modulation of dopamine and serotonin neurons being important for the development of behavioral sensitization to cocaine (Li et al., 2000). Cloning and characterization of cocaine-sensitive fly dopamine transporter (dDAT) provided a concrete cocaine target on which to base genetic study of the neurophysiology of drug-induced effects in the brain (Porzgen et al., 2001).
In order to identify genes functionally involved in the modulation of brain functions, the ideal approach is to use a genetic screen in a genetically tractable model organism. However, the classic method used for induction of behavioral sensitization in Drosophila proved inadequate for use in a genetic screen, due to variability arising during drug delivery and the behavioral scoring procedure (McClung and Hirsh, 1998). This procedure was also time consuming because it required visual analysis of each individual fly's behavior and assignment of a score based on a descriptive scale.
To decrease the variability during delivery, two new methods were developed that each delivered precise drug concentration to individual flies. In one, abdominal injections were paired with measurement of the locomotor activity using a commercially available monitoring system (Dimitrijevic et al., 2004). This demonstrated that sensitization can be induced via repeated injections and can objectively be measured as an increase in the forward locomotion. However, cocaine injections where timeconsuming and required complex animal handling. The second method successfully decreased variability by delivering precise amounts of drug onto the fly's cuticle using an airbrush . This approach revealed that the nervous system of flies is sensitive to topical drug application; however, scoring still had to be done using the descriptive scale. Ultimately, both methods required complicated animal handling and nonstandard instrumentation, which rendered them inadequate for use in a behavioral screen.
Attempts to objectively quantify the scoring procedure were done by taking advantage of negative geotaxis, that is the fly's ability to climb vertical surface (Bainton et al., 2000;George et al., 2005). Cocaine administration can, depending on dosage, either partially or fully affect ability to climb. Scoring based on counting the number of flies that stayed at the bottom of the tube demonstrated that this approach could be used to measure sensitivity to a single exposure. This approach proved adequate for a genetic screen to identify flies with changed sensitivity, and resulted in the identification of several candidate genes that were functionally conserved in rodents (Tsai et al., 2004;Bainton et al., 2005;Schwabe et al., 2005). George et al. (2005) implemented a semi-automated procedure for bottom counting of affected flies and showed that such quantification can be used to measure behavioral sensitization after multiple exposures. Furthermore, this method has specificity to identify a circadian mutant per 0 , with the same sensitivity as the previous method that relied on visual scoring of the affected behavior (Andretic et al., 1999). However, the semi-automated bottom-counting method has never been used in a genetic screen. Potential reasons are that setting-up the bottom-counting method requires engineering expertise, the method does not improve on variability that arises from potential uneven cocaine amount that each fly receives, and quantification is population based.
Ultimately, methodological improvements in drug delivery and quantitative measurement of the consequences of drug delivery were never successfully integrated into a single highthroughput method. Other improvements were made that relied on video tracking of cocaine-induced behavior, but these methods required significant animal handling and were too time consuming for a genetic screen (Bainton et al., 2000;Gakamsky et al., 2013). Consequently, identification of novel genes involved in drug-induced neural plasticity has stalled due to the lack of an adequate behavioral test for use in a genetic screen.
Here, we present a new method of inducing locomotor sensitization to volatilized cocaine (vCOC) in flies, named FlyBong. This technique delivers a precise amount of volatilized drug to each fly, quantifies in a dose-dependent manner the locomotor activity of individual fly, and compares the change in locomotor activity of individual fly before and after the administration. Because of its high-throughput nature, it can be used in genetic screens to monitor changes in locomotor activity to single or multiple exposures of different psychoactive chemicals with motor-activating effects.

Fly Strains
All behavior experiments were performed on 3-to 5-day-old wild type (wt) flies with a CantonS background. Flies were raised on a standard cornmeal/agar medium in LD 12:12 at 25 • C, 70% humidity. Circadian mutant strains used were: per 01 , tim 01 , cyc 01 , Clk Jrk and pdf 01 generously donated by C. Helfrich-Forster and mutation in dopamine transporter, fumin (fmn) from S. Birman [21]. Flies were collected using CO2 anesthesia 1 day before the experiment, and following day were transferred using an aspirator into Drosophila Activity Monitor (DAM) system tubes.

Chemicals
Cocaine-hydrochloride was purchased from Sigma-Aldrich (≥97.5%) and 96% ethanol used for preparing 10 mg/mL stock solution was purchased from VWR.

FlyBong Procedure
The FlyBong platform involves volatilizing a specific concentration of cocaine in a three-neck glass flask and then pumping the aerosol for 1 min into a collection of polycarbonate tubes, each housing single flies in a vertical DAM. Locomotor activity is measured as the number of times that an individual fly crosses the midline of the tube per minute. When quantifying the effect of volatilized drug on the behavior of flies, the average level of locomotor activity after exposure is compared to that before drug exposure (the baseline), while specificity of the cocaine effect is determined by comparison to the locomotor activity of a population exposed to warm air without cocaine (the control). Population response involves comparing average locomotor activity of varying length of time post-exposure (up to 30 min) to the average pre-exposure, baseline or control levels.
The platform for psychostimulant administration and locomotor activity monitoring consists of two main components: a drug delivery and a locomotor activity monitoring part (Figure 1). The drug delivery part consists of volatilization chamber (a three-neck flask, 250 mL, VWR), connected on one side to an air pump (Crawfish 1800 air pump), and on the other to a Gas Distribution Manifold (TriKinetics). The locomotor monitoring part consists of a manifold that delivers air to polycarbonate tubes (with two small holes to allow airflow), housing single flies in a vertical DAM (all from TriKinetics). Every DAM system tube has food on one end to prevent starvation or dehydration of flies during the assay. Locomotor activity counts are collected every minute on a computer using a PSIU9 Power Supply Interface Unit (TriKinetics). The connection between the volatilization chamber and DAM is clamped, except during the exposure of flies to a mild stream of air or vCOC.
Cocaine is volatilized in a three-neck flask positioned in a heat cap (SAF, LabHEAT, KM-G, for 250 mL flask). The central neck of the flask is used for pipetting in the cocaine-ethanol solution. To eliminate potential effects of ethanol, cocaine dissolved in ethanol is added 4-6 h before drug administration to allow the ethanol to vaporize. The flask is then closed and heated for 8 min, at which point it reaches 185 • C, while the tube leading to the DAM monitor is clamped. After 8 min heating, the cap is turned off, the clamp removed and the aerosol is pushed into DAM for 1 min using an air pump with a flow rate of 2.5 L/min.
Multiple administrations of COC were performed using the same standard protocol as single administrations (1 min, 2.5 L/min), but with lower COC concentration (45 µg). COC was administered three times: first at 9:00 AM, second at 7:00 PM and third 9:00 AM of the following day.
To test for circadian modulation of sensitivity and locomotor sensitization, we performed the assay in constant dark conditions (DD), using the standard protocol and with 12 h between exposures. Flies previously entrained to 12 h light and 12 h dark (LD) were then released in constant dark (DD) for 1 day before the experiment. The first group of flies in DD was exposed to the first dose of COC at 10 am (2 h after light would have come on in LD) and to the second at 10 pm (2 h after light would have come on in LD). The second group received the first dose at 10 pm and the second at 10 am. All experimental manipulations were performed under dim red light.

Cocaine Measurements
To test the homogeneity of distribution and stability of COC after volatilization, as well as the amount delivered to the individual flies we measured amount of COC in the tube without the fly, and separately the amount of COC on the fly. COC amount inside the tube was determined by using empty tubes without flies or food, connected to the FlyBong on one side and immersed in 2 mL Eppendorf tubes filled with 500 µL of distilled water on the other side. We applied standard protocol (8 min of volatilization, 2.5 L/min air flow, 1 min of drug delivery, and 75 µg of COC). After 5 min, all tubes were detached from the dispenser and washed with an additional 500 µL of distilled water. Eppendorf tubes with volatilized samples were additionally vortexed for 1 min at 2,500 rpm. The monitor and dispenser are arranged with four rows and eight columns in each row, so that for all 32 samples the UV-VIS spectra was recorded in triplicate. Five hundred microliters of each sample was recorded using a 48-well plate and microplate reader TecanInfinitePro200 at λ max = 275 nm, is the maximum absorbance for COC in UV-VIS spectra.
To measure COC amount on the surface of the fly we applied standard protocol to 32 flies housed in individual tubes of DAM monitor. Five min after the exposure each fly was frozen at −20 • C. Each fly was then transferred to the 2 mL Eppendorf tube filled with 300 µL of distilled water. Tubes containing flies were vortexed for 1 min at 2,500 rpm. Two hundred and fifty microliters of each sample was recorded in triplicate using a 48-well plate and microplate reader TecanInfinitePro200 at λ max = 275 nm.
Concentration of COC in the samples (on the flies or inside the tube) was determined using COC calibration curve. Calibration curve was determined from standards with serial dilution of FIGURE 1 | FlyBong platform for measuring changes in locomotor activity of Drosophila after delivery of vCOC. Cocaine dissolved in ethanol is pipetted into three-neck flask, which is then heated to the cocaine volatilization temperature. Single flies are housed in the individual tubes of the vertical Drosophila monitor (TriKinetics) and exposed to vCOC for 1 min by turning on the air pump and removing the clamp. Their locomotor activity is monitored as the number of crossings of the midline of the tube per minute for 30 min before and 30 min after vCOC exposure.
COC concentration by measuring the absorbance at 275 nm. Using the linear regression method we derived the equation for determining COC concentration in the samples. To test the stability of COC after volatilization, we performed UV-VIS on random triplicate of COC samples after volatilization using Cary 60 UV-VIS in range from 200 to 800 nm and compared it to COC standard made from COC that has not undergone 8 min heating procedure.

Data Analysis
The DAM system collects data as a number of times that an individual fly crosses the midline of its tube in 1 min, which allows the data to be analyzed at either a population or an individual fly level. For analysis of population data, data was collected for 30 min before and 30 min after drug delivery (heating and drug delivery time are excluded from this analysis), allowing calculation of average counts per minute for 32 flies. Data can then be plotted as an average of counts per minute for 32 flies in different time intervals before and after administration.
Individual fly response compares average counts per minute of an individual fly in the first 5 min before (baseline) and after cocaine administration (excluding heating and drug delivery time). The baseline locomotor activity is then compared to activity after administration and the activity is qualified as either: the same, decreased, increased or other (flies with inconsistent changes after subsequent administrations). The number or percentage of flies with an increased activity after first drug exposure, compared to their baseline, represents a measure of sensitivity (SENS) to cocaine. After the second drug administration, locomotor activity after administration is compared to locomotor activity after first exposure. The number or percentage of flies that develop locomotor sensation (LS), is based on whether each individual fly displays increased locomotor activity after first administration, relative to baseline, and a further increase after the second administration, relative to the first. These same criteria were used for calculating locomotor sensitization of individual flies after multiple doses of psychostimulant.

Statistical Analyses
Statistical analyses of population data were performed using Student's t-test for independent (between groups) or dependent (within same group) samples. Individual data was statistically tested using χ 2 test. Differences were considered significant if probability of error was ≤0.05.

Protocol Optimization
To develop the standard protocol, which consists of heating the flask for 8 min, followed by cocaine delivery for 1 min using 2.5 L/min airflow, we performed a series of control experiments. These experiments showed that changes in locomotor activation following cocaine administration are specific to the activating effects of cocaine and not dependent on other environmental factors, as detailed below.
Firstly, we compared the locomotor activity of undisturbed group to groups that received either air pumped in without heating of the flask, heated air as in the standard protocol or heated air with un-vaporized ethanol. Ethanol is used to dissolve cocaine and to deliver it to the flask, however in all experiments, we wait for the ethanol to vaporize before heating the flask. Neither of these conditions led to a significant change in the baseline activity (Figure 2).
Cocaine hydrochloride starts to volatilize at around 185 • C. For our protocol, we discovered that the three-neck flask, has to be heated for 8 min in order for the internal temperature to reach 185 • C (Supplementary Figure 1A). At this time point the flasks fills with a visible cloud of cocaine aerosol.
To test if the distribution of cocaine was homogenous between each tube of the recording monitor we measured the FIGURE 2 | Control experiments show minimal disturbance of locomotor activity in wt males. Kinetic line graphs of locomotor activity in different control groups. Group no treatment represent flies with normal activity without any experimental exposures, air − 2.5 L/min air flow for 1 min, warm air − 2.5 L/min air flow for 1 min, following 8 min of heating, warm air + EtOH − 2.5 L/min air flow for 1 min following 8 min of heating of 75 µL of 96% ethanol. Experiments n = 32 flies per group, were repeated for each group three times. Light gray panel represents locomotor activity 5 min before exposure for all groups, dotted line is time of the exposure, and darker gray panel denotes 5 min after exposure. Data are plotted as mean + SEM of counts/min, with a resolution of 1 min. amount of cocaine residues in each tube and on each fly (see Materials and Methods), and determined using calibration curve (Supplementary Figure 1B) that the concentration of cocaine in the tubes did not depend on tube location and that each fly received to the same amount of vCOC (Supplementary Figure 1C). Thus, the individual differences in the amount of locomotion that vCOC induces cannot be explained with the variability of the amount of delivered cocaine. To confirm that volatilization procedure did not caused decomposition of COC we compared UV VIS spectra of cocaine samples from the experiment, to cocaine standard and determined that only peaks at 230 and 275 nm were present in all spectra (Supplementary Figure 1D).
We tested several intensities of airflow without cocaine or heating, in order to determine potential effect of airflow on locomotor activity. The maximal airflow that could be used without causing an increase in the locomotor activity of the flies was 2.5 L/min (Supplementary Figure 2). To determine the optimal duration of airflow, we heated the flask without cocaine for 8 min, activated the air pump at a flow rate of 2.5 L/min and exposed flies to warm airflow for various time periods of between 10 s and 7 min (Supplementary Figure 3A). One-minute long exposure time led to the least disturbance in locomotor activity of the flies, with no significant difference from baseline activity in 5 min before exposure (Supplementary Figure 3B). In contrast, durations shorter or longer than 1 min led to a significant increase in locomotor activity (Supplementary Figure 3B). We suspect that shorter exposures (<1 min) cause a startle-like response, while longer exposures (>1 min) decreased humidity and increased temperature in the recording tubes, causing increased locomotion.

Acute Administration of vCOC Increases Locomotor Activity
To test if exposure to vCOC has dose-dependent motoractivating effects, we exposed flies to vCOC concentrations FIGURE 3 | Exposure to vCOC transiently and dose-dependently increases locomotor activity. (A) Kinetic graph of locomotion expressed as number of counts per minute for a control group of flies exposed to warm air (ctrl) and of groups exposed to vCOC concentrations of 25 µg (COC 25 µg), 75 µg (COC 75µg), or 150 µg (COC 150µg). Experiments n = 32 flies per group, were repeated for each group three times The light gray panel indicates the 5 min immediately prior to exposure, the dotted line is the time of exposure and the dark gray panel indicates 5 min after exposure. (B) Mean locomotor activity + SEM for vCOC concentrations ranging from 25 to 150 µg (n = 32 flies per treatment) for the 5 min immediately before and after cocaine exposure. Statistical significance (p ≤ 0.05) indicated by a: within group comparison of locomotion before and after administration (t-test for dependent samples); b: between group comparison for 5 min after exposure (t-test for independent samples). (C) Changes in level of locomotor response of individual flies where amount of locomotor activity for individual fly after exposure to 75 µg of vCOC (8 min heating, 1 min exposure and 2.5 L/min air flow) or warm air control (ctrl) (n = 32 for each group). Changes are categorized as increase, decrease or no change, based on locomotion in the 5 min immediately after exposure, compared to the 5 min before. * χ 2 test statistical significance (p ≤ 0.05) between group comparison. ranging from 25 to 150 µg using FlyBong. Flies that were exposed to cocaine displayed increased locomotor activity in the first minute after cocaine delivery had ceased, and locomotion remained significantly elevated for 5 min (Figure 3A). This increase is statistically significant when compared to the pre-exposure baseline level and levels in the control group, Student's t-test for dependent samples p < 0.05 ( Figure 3B). After 5 min, levels of locomotor activity were once again similar to pre-exposure baseline and to the post-exposure levels in the control group, indicating a transient effect of acute cocaine administration on locomotion.
Although flies responded with an increased amount of locomotion to the lowest dose (25 µg) of vCOC, we observed a robust response to 75 µg vCOC, while higher doses did not lead to further significant increases in locomotion ( Figure 3B). The absence of a wider range of linear dose-dependent increases in locomotor activity likely reflects the appearance of motor behaviors, besides locomotion. Depending on the dose, vCOC can induce a range of hyperactive and stereotypical behaviors (McClung and Hirsh, 1998), that would not lead to linear increases in locomotion as measured by DAM monitors.
We next sought to determine the extent to which individual responses correspond or contributed to the population average, by analyzing locomotor activities of individual fly during the 5 min interval immediately before (baseline) or after exposure to 75 µg of vCOC. We categorized each locomotor response as either an increase, decrease, or no change when compared to baseline levels. The majority of individual flies respond with an increased locomotion after cocaine exposure (69%), and only a few (6%) with a decrease, a statistically significant difference p < 0.05 χ 2 test, while the remaining showed no change in level of locomotion (25%) (Figure 3C). Flies with an increased response represent individual sensitivity to motor-activating effects of cocaine, and the percentage of flies with such as response in this experiment demonstrates that cocaine had a motor activating influence in the majority of flies. In the control group, which was exposed to warm air only, the percentage of flies that increased, decreased or did not change their locomotion was approximately equal, suggesting a random effect ( Figure 3C).
Female flies showed distinct locomotor responses to acute vCOC when compared to males. Similar to males, their locomotor activity is transiently increased immediately after exposure, and shows a dose-dependent increase in locomotion when exposed to 75 and 100 µg of cocaine (Supplementary Figures 4A,B). However, female flies showed altered locomotor activity following 1 min exposure to warm airflow alone, in contrast to the males, and it is therefore not clear what proportion of their response is cocaine specific (Supplementary Figures 5A,B and 4B). Furthermore, analysis of the responses of individual flies showed that their sensitivity to warm airflow alone is similar to their sensitivity to vCOC (Supplementary Figures 4B,C). These data demonstrate a pronounced sexual dimorphism in the motor-activating effects of cocaine in Drosophila, as reported previously (McClung and Hirsh, 1998). For that reason all currently published reports on the behavioral effect of vCOC are done using only male flies. Since in our experiments males also show a reliable and cocaine-specific increase in locomotion after exposure, all further experiments were performed using male flies only.

Repeated Administration of vCOC Leads to Locomotor Sensitization
Frontiers in Molecular Neuroscience | www.frontiersin.org FIGURE 4 | Continued exposed to warm air and a group exposed twice to vCOC (COC 75 µg) using our standard protocol, with the first administration at 9:00 AM and the second at 3:00 PM. Data are plotted as mean of population (32 flies) locomotor activity 5 min before and 5 min after (1st and 2nd) exposure to COC, +SEM. Statistical significance (p ≤ 0.05) indicated by: c: comparison of bsl with 1st; d: 1st with 2nd; e: bsl with 2nd (within group comparison using t-test for dependent samples). (D) Analysis of individual flies from data in part (A). Percent of individual flies in a population that show sensitivity (SENS) and locomotor sensitization (LS) in control (CTRL) and group exposed to vCOC (COC 75 µg). * χ 2 test statistical significance p ≤ 0.05. Starting data were same as (C) from which categories increase, decrease and same were divided.
if we can induce and measure locomotor sensitization using FlyBong, we exposed wt male flies to two doses of vCOC using the standard protocol (75 µg of cocaine, 8 min of heating, 1 min exposure and 2.5 L/min air flow), but using variable time intervals between the exposures, ranging from 3 to 30 h. The first administration was always at 9:00 AM, 1 h after the lights were switched on. A significant increase in the amount of locomotor activity in response to the second dose, when compared to the first, was present when administrations were given between 6 and 24 h apart, Student's t-test for dependent samples p < 0.05 (Figures 4A,B), indicating that flies develop locomotor sensitization to vCOC. Locomotor sensitization is a long-term change in the responsiveness of the nervous system, and its presence in these flies is supported by the observation that the earliest time point at which significant locomotor sensitization occurs is 6 h after the first exposure. At earlier time points, we observed a decrease in locomotor activity, suggesting that the time interval was insufficient for expression of locomotor sensitization. Locomotor sensitization can emerge from the dynamics of locomotor responses of a population (Figure 4C), however, it is not known to what extent the behavioral sensitization of individual flies contributes to the population response. We define locomotor sensitization of an individual fly as an increase in locomotor activity after the first administration (compared to baseline) combined with a further increase following the second administration (compared to the first). According to these criteria, 40% of the flies showed locomotor sensitization to vCOC (Figure 4D) compared to controls where only 6% of flies showed locomotor sensitization, a statistically significant difference p < 0.05, χ 2 test. Thus, while sensitivity (increased locomotor responding to a single dose) is a prevalent response in a population of the flies (69% flies), only a subgroup of those flies (40%), could be seen to develop locomotor sensitization ( Figure 4D).
We applied the same standard protocol to wt females but did not observe the appearance of locomotor sensitization. At the population level, the second administration of cocaine did not induce an increase in locomotor activity, and analysis of individual flies shows that the experimental group did not differ significantly from the control (Supplementary Figures 6A,B). These results do not necessarily imply that locomotor sensitization cannot be induced in females, but rather that our standard protocol is not optimized for females.
To test if flies will continue to increase their locomotor activity following multiple administrations, we exposed flies to three intermittent doses of vCOC. Analysis of the population shows that flies continue to increase their locomotor activity following the third exposure ( Figure 5A), however, an important characteristic of individual response is not obvious from the population data. Specifically, the percentage of flies that are sensitive to each given administration is similar for all three doses and varies by around 50%, but of these, only a subpopulation (22%) showed the consecutively increased locomotion to each dose that would indicate locomotor sensitization in this assay ( Figure 5B).

Locomotor Sensitization to vCOC Depends on Functional Circadian Genes and Dopaminergic Transporter
Studies in both Drosophila and laboratory rodents have shown that the genes that control circadian rhythmicity are involved in the regulation of motor activating and in arousing effects of psychostimulants (Andretic et al., 1999(Andretic et al., , 2005. To validate FlyBong and to measure locomotor sensitization we exposed light:dark entrained flies with mutantions for circadian genes per 01 , tim 01 , Clk Jrk and cyc 01 to vCOC. We also include flies with a mutation in a gene for a neuropeptide pigment dispersing factor (pdf ), that conveys signals from circadian pacemaker cells in the fly brain to the rest of the brain and body (Renn et al., 1999). PDF-positive neurons also express dLmo gene, a regulator of LIM-homeodomain proteins, identified as a regulator of cocaine sensitivity in Drosophila (Tsai et al., 2004).
At the population level we confirm that the per, Clk and cyc genes are required for locomotor sensitization to vCOC. Exposure to the standard protocol with two doses of vCOC 6 h apart (first administration at 9:00 AM, second at 3:00 PM) did not lead to increased locomotor activity to successive doses of vCOC in per 01 , Clk Jrk and cyc 01 mutants when locomotor activity after first exposure was comparied to locomotor activity after second exposure using t-test for dependent samples (Figure 6A). We also confirm that tim 01 males behave similar to wt, suggesting that tim gene is not required for development of behavioral sensitization (Figure 6A). At the population level we also observe lack of locomotor sensitization in per 01 mutant males. Consistency between individual and population level analysis depended on the mutant strain (Figures 6A,B). Individual analysis confirmed the importance for per, Clk and cyc genes and showed that the lack of locomotor sensitization in Clk Jrk flies is accompanied by a decreased sensitivity to the first administration of vCOC determined using χ 2 test ( Figure 6B). However, there was a discrepancy between population and individual level analysis of pdf 0 mutants. While per 01 flies did not develop locomotor sensitization at the population level, analysis of individual flies showed the contrary (Figures 6A,B).
To determine the origin of this discrepancy, we analyzed population and individual response of all flies to two exposures of warm air. The Clk Jrk mutant was similar to wt flies and is not sensitive to, nor develops locomotor sensitization to, the warm air (Figures 6C,D). Over 50% of the per 01 , cyc 01 , tim 01 and pdf 01 populations showed sensitivity to warm air, while over 30% of tim 01 and pdf 01 mutant flies develop locomotor sensitization to warm air ( Figure 6D). These data suggest that the putative locomotor sensitization of tim 01 and pdf 01 mutants to cocaine has to be reexamined, as at least part of that phenotype is not cocaine-specific. Especially in regard to pdf 01 mutants, where population and individual results diverged, there is the need for further examination of the interaction between the output from the central circadian pacemaker and the regulation of locomotor sensitization to vCOC. Very low fraction of per 01 , Clk Jrk and cyc 01 flies develops locomotor sensitization to vCOC as does to warm air (Figure 6), arguing for impaired mechanisms of neuronal plasticity in these mutants. Individual level analysis of FlyBong locomotor output and use of appropriate controls offers new information about behavioral responses, that would not be obvious when examining only the average population response to vCOC.
Our data suggests that the ability to develop locomotor sensitization depends on the presence of functional circadian genes, possibly regulating circadian behavior separately from drug-induced behavior. However, this does not exclude a rhythmic circadian modulation of sensitivity to an acute dose FIGURE 6 | Locomotor sensitization to vCOC depends on circadian genes. (A) Average locomotor activity of flies 5 min before drug exposures (bsl), 5 min after first exposure (first) and 5 min after second (second) exposure to 75 µg of vCOC given 6 h apart. Fly populations were either wild type (wt) or mutant for circadian genes Clk Jrk , per 01 , cyc 01 , tim 01 and pdf 01 (n = 32 for each group). Statistical significance (p ≤ 0.05) indicated by: c: comparison of baseline to after first administration; d: after first to after second and e: baseline to after second administration (all within the group using t-test for dependent samples). (B) Percentage of individual flies showing sensitivity or increasing locomotor activity to the first exposure of vCOC (bsl vs. 1st) and flies showing further increase in locomotor activity to the second exposure (LS) (bsl vs. 1st vs. 2nd). χ 2 test showed statistical significance p ≤ 0.05 for comparison of wt to mutants in SENS (#) and ( * ) LS. Starting data were same as (A) from which categories increase, decrease and same were divided. (C) Average locomotor activity (counts/min) during baseline, 5 min before (Continued) FIGURE 6 | Continued exposures (bsl), 5 min after first exposure (1st) and 5 min after second (2nd) exposure to warm air flow (2.5 L/min, for 1 min after 8 min of heating), given 6 h apart. Fly populations were either wild type (wt) or mutants for circadian genes Clk Jrk , per 01 , cyc 01 , tim 01 , and pdf 01 (n = 32 for each group). Statistical significance (p ≤ 0.05) indicated by: c: comparison of baseline activity to after first administration; d: comparison of activity after first and second exposures; e: comparison of baseline activity to after second administration. All tests were within the group, using t-test for dependent samples. (D) Percentage of individual flies showing sensitivity or increased locomotor activity to a first exposure to warm air flow (2.5 L/min, for 1 min after 8 min of heating) and flies showing further increase in locomotor activity to a second exposure (LS). χ 2 test showed statistical significance (p ≤ 0.05) for comparison of wt to mutants in SENS (#) and ( * ) LS. Starting data were same as (C) from which categories increase, decrease and same were divided.
FIGURE 7 | Time of day modulates locomotor sensitization, without affecting sensitivity to the first exposure. (A) Average locomotor activity (counts/min) during baseline, 5 min before drug exposures (bsl), 5 min after first exposure (1st) and 5 min after second (2nd) exposure to 75 µg of vCOC given 12 h apart for of a population of wild type (wt) (n = 32 for each group). Statistical significance (p ≤ 0.05) indicated by: c: comparison of baseline to after first administration; d: after first to after second; e: baseline to after second administration (all within the group using t-test for dependent samples).
(B) Percentage of individual flies showing increase in locomotor activity to the first exposure of vCOC compared to baseline (SENS 1st), percentage of flies that show increase in locomotor activity to the second exposure, relative to the first (SENS 2nd), and a subgroup that shows increase after first and further increase after second exposure (LS). Starting data were same as (A) from which categories increase, decrease and same were divided.
of cocaine, or neuronal plasticity after multiple doses. To test if sensitivity or locomotor sensitization is influenced by circadian modulation, we administered two doses of vCOC with a 12-h interval. These experiments were performed in constant darkness to remove the confounding factor of light and to demonstrate if the source of modulation is the activity of the endogenous circadian clock. Sensitivity to the acute dose did not vary as a function of the circadian time, as the percentage of flies that responded with increased activity at 10:00 AM did not vary significantly FIGURE 8 | Locomotor sensitization to vCOC is dependent on a functional dopaminergic transporter. (A) Average locomotor activity (counts/min) during baseline, 5 min before drug exposures (bsl), 5 min after first exposure (1st) and 5 min after 2nd) exposure to 75 µg of vCOC given 6 h apart for a population of wild type (wt), and flies mutant in dopaminergic transporter, fumin (fmn) (n = 32 for each group). Statistical significance p ≤ 0.05; c: comparison of baseline to after first administration, d: after first to after second and e: baseline to after second administration (all within the group using t-test for dependent samples). (B) Percent of individual flies increasing locomotor activity to first exposure of vCOC (bsl vs. 1st) and flies showing further increase in locomotor activity to the second exposure (LS) (bsl vs. 1st vs. 2nd). χ 2 test showed statistical significance p ≤ 0.05 ( * ) for wt compared to fmn in LS. Starting data were same as (A) from which categories increase, decrease and same were divided. from 10:00 PM, χ 2 test (Figures 7A,B). The same result was obtained from both population and individual level analysis. Interestingly, fewer flies showed an increased response to the second dose (relative to the first) when it was given at 10:00 PM, which consequently resulted in a weaker locomotor sensitization. Thus, expression of locomotor sensitization is weaker during the subjective night, than during the subjective day. This shows that circadian clock modulates cocaine induced neuronal plasticity, but not sensitivity to cocaine.
Sensitivity to the motor-activating effects of cocaine in mammals depends on a functional dopaminergic transporter (DAT), a direct molecular target for cocaine (Chen et al., 1992;Giros et al., 1996;Mead et al., 2002;Makos et al., 2010). Drosophila with a mutation in a DAT gene named fumin (fmn), are hyperactive and have reduced sleep due to a decreased dopamine uptake into the presynaptic cell (Kume et al., 2005;Makos et al., 2010;Ueno and Kume, 2014). To further validate our platform and to show that motor-activating effects of vCOC that we measure depend on functional DAT we exposed fmn flies to two doses of vCOC, 6 h apart. Population level analysis showed that fmn flies indeed have increased basal levels of activity when compared to wt controls, and are not sensitive to acute exposure to 75 µg of vCOC, Student's t-test for dependent samples ( Figure 8A). A second cocaine exposure leads to a decrease in the average amount of locomotor activity. These data suggest that a functional DAT protein is required both for sensitivity to cocaine and for development of locomotor sensitization ( Figure 8A). However, individual level analysis showed that the flies sensitivity to the first cocaine administration is preserved ( Figure 8B). As above for the pdf mutant, this can be explained by increased sensitivity to the warm air (Supplementary Figures 7A,B), which is in accordance with the heightened arousal of these flies.

FlyBong Is Suitable for Genetic Screens and Selection Experiments
We have developed a new method for quantifying vCOC-induced changes in the locomotor activity of Drosophila, that we name FlyBong. The method consists of a novel drug delivery system, connected to a commercially available platform for quantifying the locomotion (TriKinetics). Using FlyBong we can, with minimal animal handling: (a) deliver precise drug amount to individual flies, (b) objectively quantify the level of locomotor activity both before and after drug administration, (c) quantify the change in locomotor activity of either single flies or the whole population, (d) record locomotor activity of 32 flies simultaneously. With only a moderate financial investment, FlyBong could easily be scalable up, allowing it to be used in genetic screens in which hundreds of flies could be tested per day. These characteristics make FlyBong significantly more objective, efficient and versatile, as well as higher throughput, than previously described methods.
The most important improvement of FlyBong, relative to previous systems, is that it provides both population and individual measures of locomotor activity. We have shown that there is individual variability in flies' sensitivity to motor-activating effects of cocaine, and that only a fraction of these sensitive flies go on to develop locomotor sensitization. This suggests that neural mechanisms that govern sensitivity are distinct from those that regulate locomotor sensitization. Several results described here support this conclusion: First, three consecutive exposures to vCOC each led to similar percentage of flies showing sensitivity, while the percentage of flies that develop locomotor sensitization to the three exposures is much lower. Second, sensitivity does not vary as a function of circadian time, while locomotor sensitization does. Third, although sensitivity of per and cyc mutant flies is similar to that of wt flies, they fail to develop locomotor sensitization.
The reason that vCOC was initially selected as a drug of choice for Drosophila was that it led to dramatic and concentrationdependent changes in motor behavior, as exemplified by the appearance of uncoordinated, uncommon or stereotypical behaviors (McClung and Hirsh, 1998). Increases in forward locomotion were observed at lower doses, and has been quantified using methods based on oral administration of cocaine or its injection into the abdomen (Dimitrijevic et al., 2004;Lebestky et al., 2009). Transition from increased locomotion into uncoordinated or stereotypical behavior is dose-dependent.
As we have used a DAM monitor to quantifying locomotor activity, we are aware of these limitations and have identified cocaine concentrations that lead to dose-dependent change in locomotor activity, but not in other uncoordinated behaviors. We have unpublished evidence that other psychoactive compounds with a predominant locomotor effect, lead to more significant dose-dependent and sensitizing effects.
The 75 µg does of vCOC used in our experiments is the same as has been used in other experiments in which it was delivered from a heated filament positioned in close proximity to a group of 10 flies in a vial Hirsh, 1998, 1999;Andretic et al., 1999;Li et al., 2000;Park et al., 2000). In FlyBong, cocaine is volatilized in a flask and delivered via tubing and a dispenser to individual flies in recording tubes, although a proportion of vCOC instead condenses on the surfaces through which it flows before it reaches the recording tubes. We have therefore measured residual cocaine on the surface of the recording tubes and showed that less than 10% of the volatilized amount reaches the individual tube. This quantity is within the range of concentrations that predominately induces locomotor activation. Absence of a wider ranging dose-response curve using FlyBong argues for the appearance of uncoordinated behaviors at higher doses, which in our system cannot be recorded as an increase in locomotion.
Delivering a precise drug amount to individual flies within a population had not been achieved using previously published methods (McClung and Hirsh, 1998;Bainton et al., 2000Bainton et al., , 2005George et al., 2005). Attempts to deliver a known amount of drug via injection or an air brush were not simple and required sophisticated animal handling (Dimitrijevic et al., 2004;Lease and Hirsh, 2005). FlyBong involves lower amounts of animal handling than these approaches and consequently reduces both testing time and experimental error due to animal manipulation. We place flies into recording tubes 1 day before testing in order to habituate them to the environment, and they are then not moved or manipulated for the duration of the experiment. Here, we have shown that the drug concentration received by each is equivalent, as the amount of drug per tube is independent of recording tube location within the monitor, and does not vary significantly between experiments. Thus the behavioral variability that we see in sensitivity to acute cocaine exposure and locomotor sensitization to two repeated exposures, where response is present only in a fraction of the population, likely represents genetic and environmental influences that occur before testing. This therefore suggests that FlyBong can be used for the selective breeding of flies that are either sensitive and develop locomotor sensitization versus those that do not, in order to identify the potential genetic contributions for these behaviors, as an alternative to a genetic screen.

FlyBong Identifies Cocaine-Specific Behavioral Phenotypes of Genetic Mutants
We have shown in several mutant strains that analysis of individual flies aids in differentiating cocaine-specific from non-specific effects on locomotor activity. We identify fmn mutant as hyperactive at the population level and with increased arousal in response to the control, warm airflow, in agreement with previously reported behavioral phenotypes of this mutant (Kume et al., 2005). Sensitivity to vCOC at individual level is not cocaine-specific, in agreement with previously published reports of a lack of sensitivity to cocaine administration in dopaminergic knockout mice (Chen et al., 1992;Giros et al., 1996;Mead et al., 2002) and with results of electrophysiological studies on the function of DAT in Drosophila (Makos et al., 2010). This shows that FlyBong can identify mutants that show baseline hyperactivity, and can furthermore distinguish between cocainespecific versus non-specific arousal.
This is further exemplified with our results of increased individual sensitivity to warm air in per 01 , cyc 01 , tim 01 and pdf 01 flies. The increased sensitivity phenotype in cyc 01 and per 01 mutants is separate from their inability to develop locomotor sensitization. Both at the population level and at the individual level they do not develop locomotor sensitization to vCOC, nor do they develop locomotor sensitization to control, warm air. Clk Jrk mutant flies show cocaine-specific decrease in sensitivity, accordance with the inhibition of locomotor activity by light in Clk Jrk mutant flies (Lu et al., 2008), and inability to develop locomotor sensitization to vCOC. Thus, per 01 , cyc 01 , and Clk Jrk lack ability to modulate neuronal activity in response to repeated exposures to vCOC, which is in case of Clk Jrk accompanied with specific decrease in sensitivity. This shows separate molecular mechanisms involved in the regulation of behavioral phenotype to the acute versus multiple exposures of vCOC, which can be precisely measured using FlyBong.
Individual analysis identified an increased sensitivity to cocaine in pdf 01 and tim 01 mutants, which is in accordance with the role of pdf in regulating light-mediated arousal (Renn et al., 1999;Shimada et al., 2016) and promoting of locomotor activity by light in tim 01 mutants (Lu et al., 2008). However, locomotor sensitization to vCOC in pdf 01 and tim 01 flies is not cocaine-specific and will require further study in order to determine the role that these genes play in neuronal plasticity. Since both mutants have increased sensitivity to the warm air and develop locomotor sensitization to the warm air, future studies will have to use an alternate mode of delivering COC. Our results from population level analysis of pdf 01 show normal sensitivity to vCOC, similar to normal sensitivity measured as negative geotaxis of a population of pdf 01 mutants to vCOC (Tsai et al., 2004). However, our analysis of individual flies defined new phenotypes that were not immediately obvious in a population. Thus, analysis of the locomotor activity of individual flies, as well as their respective controls, significantly aids in differentiating cocaine-specific from non-specific effects. As many genetic mutations can have pleiotropic effect on arousal, individual analysis could aid in identifying currently unknown arousal phenotypes in other genetic mutants.
We have developed, characterized and validated FlyBong using environmental and genetic manipulations. In the future, Fly Bong can therefore be used to further dissect the genetic contribution to mechanisms that govern sensitivity, along with those that are involved in the processes of neuronal plasticity, which underlie locomotor sensitization. Statistical analysis of a population versus individual response further facilitates different approaches aimed at unbiased gene discovery, such as genetic screens or selective breeding. This will aid in understanding the mechanisms by which addictive drugs can change brain functioning and behavior not only in Drosophila, but in other laboratory animals and humans.