Macromolecular crowding limits growth under pressure

Baptiste Alric 1 , Cécile Formosa-Dague 2 , Etienne Dague 3 , Liam J. Holt 4, † & Morgan Delarue 1, † 1 MILE team, CNRS, UPR8001, LAAS-CNRS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France 2 TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France 3 ELIA team, CNRS, UPR8001, LAAS-CNRS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France 4 New York University Grossman School of Medicine, Institute for Systems Genetics, 435 E 30th Street, New York, NY, United States † to whom correspondence should be addressed: liam.holt@nyulangone.org; morgan.delarue@laas.fr

Cells that grow in confined spaces eventually build up mechanical compressive stress. This growth-induced pressure (GIP) decreases cell growth. GIP is important in a multitude of contexts from cancer, to microbial infections, to biofouling, yet our understanding of its origin and molecular consequences remains limited. Here, we combine microfluidic confinement of the yeast Saccharomyces cerevisiae, with rheological measurements using genetically encoded multimeric nanoparticles (GEMs) to reveal that growth-induced pressure is accompanied with an increase in a key cellular physical property: macromolecular crowding. We develop a fully calibrated model that predicts how increased macromolecular crowding hinders protein expression and thus diminishes cell growth. This model is sufficient to explain the coupling of growth rate to pressure without the need for specific molecular sensors or signaling cascades. As molecular crowding is similar across all domains of life, this could be a deeply conserved mechanism of biomechanical feedback that allows environmental sensing originating from the fundamental physical properties of cells.

Cells in every kingdom of life can proliferate in spatiallylimited environments. In metazoans, tissues have physical boundaries [START_REF] Sebastian J Streichan | Spatial constraints control cell proliferation in tissues[END_REF]. In plants, roots sprout into a solid ground [START_REF] Ag Bengough | A biophysical analysis of root growth under mechanical stress[END_REF][START_REF] Fal | Tissue folding at the organ-meristem boundary results in nuclear compression and chromatin compaction[END_REF]. In microbes, substrate adhesion physically limits colony expansion [START_REF] Rivera-Yoshida | Microbial multicellular development: mechanical forces in action[END_REF][START_REF] Asally | Localized cell death focuses mechanical forces during 3d patterning in a biofilm[END_REF][START_REF] Trejo | Elasticity and wrinkled morphology of bacillus subtilis pellicles[END_REF]. To proliferate in confinement, cells must push on the boundaries of their environment and neighboring cells, leading to development of compressive forces that translate, at the multicellular scale, into the buildup of a mechanical growthinduced pressure, hereafter denoted GIP. GIP decreases cell growth and division of all organisms: bacteria, fungi, plants or mammals [START_REF] Philip | Microbial growth in a fixed volume: studies with entrapped escherichia coli[END_REF][START_REF] Chu | Self-induced mechanical stress can trigger biofilm formation in uropathogenic escherichia coli[END_REF][START_REF] Delarue | Self-driven jamming in growing microbial populations[END_REF][START_REF] Helmlinger | Solid stress inhibits the growth of multicellular tumor spheroids[END_REF][START_REF] Alessandri | Cellular capsules as a tool for multicellular spheroid production and for investigating the mechanics of tumor progression in vitro[END_REF][START_REF] Ilaria | Mechanical control of cell proliferation increases resistance to chemotherapeutic agents[END_REF]. However, the mechanisms that control proliferation under GIP remain unknown. In particular, it is unclear whether growth reduction is due to specific signaling pathways, or is a necessary consequence to changes of the physical properties of the cells. Some signaling pathways have been associated with survival or division under GIP [START_REF] Nam | Cell cycle progression in confining microenvironments is regulated by a growth-responsive trpv4-pi3k/akt-p27kip1 signaling axis[END_REF]14], but it remains unclear if these pathways affect growth per se. For example, mutants in the SCWISh network tend to lyse due to mechanical instabilities associated with budding, but their ability to develop GIP is unperturbed [14].

On the other hand, mechanical perturbations to cells also influence fundamental physical parameters. One such parameter is macromolecular crowding, which relates to the high packing fraction of macromolecules in the cell, and can decrease biochemical reaction rates due to decreased effective diffusion [START_REF] Miermont | Severe osmotic compression triggers a slowdown of intracellular signaling, which can be explained by molecular crowding[END_REF][START_REF] Vibhute | Transcription and translation in cytomimetic protocells perform most efficiently at distinct macromolecular crowding conditions[END_REF][START_REF] Nettesheim | Macromolecular crowding acts as a physical regulator of intracellular transport[END_REF][START_REF] Fernandez-De Cossio-Diaz | A physical model of cell metabolism[END_REF]. However, the role of crowding in response to mechanical stress in general, and GIP in particular, has been largely overlooked.

In this Letter, we investigated the relationship between growth-induced pressure, macromolecular crowding and cell growth in the budding yeast S. cerevisiae. Our results are best explained by a model in which the rates of intracellular osmolyte production and macromolecular biogenesis are intrinsically coupled. To develop GIP, osmolytes and macromolecules are produced, while cell expansion is limited, causing the cell interior to become crowded, leading to a biophysical feedback that limits cell growth.

We used microfluidic elastic chambers as a model confining 3D environment (Fig. 1a, Methods) [START_REF] Holt | Mechano-chemostats to study the effects of compressive stress on yeast[END_REF]. After filling the chamber, cells pushed against their neighbors and onto their surroundings. Cells were continually fed through microchannels to prevent nutrient depletion and enable switching of media. After 10 hours of confined growth, the elastic chamber was quite deformed, almost doubling in volume. This deformation was used to measure the amount of growth-induced pressure, GIP, developed by the cells [START_REF] Delarue | Self-driven jamming in growing microbial populations[END_REF]14]. We posited that, under confinement, GIP resulted from an increase in intracellular osmotic pressure, which was balanced not only by the cell wall but also by the surrounding effective elasticity of the other cells and the PDMS chamber.

In response to osmotic stress, water loss from cell leads a decreased cell volume, which increases the concentration of intracellular osmolytes. We can subdivide osmolytes into two classes, small and large, that we operationally define by their ability to freely diffuse across the nuclear pore, a cutoff value of ∼ 3 nm hydrodynamic radius [START_REF] Görlich | Transport between the cell nucleus and the cytoplasm[END_REF]. The concentration of small osmolytes is dominated by ions and metabolites such as glycerol, while large osmolytes are macromolecules such as proteins, ribosomes and mRNA. Changes in nuclear volume are indicative of changes in cytosolic macromolecular concentration. Under osmotic stress, there is a proportional reduction of the nuclear volume, keeping the nuclear/cytoplasmic volume ratio constant as in [START_REF] Petelenz-Kurdziel | Quantification of cell volume changes upon hyperosmotic stress in saccharomyces cerevisiae[END_REF] (SI Fig. 1), indicating a relative increase in the concentration of cytoplasmic macromolecules.

Remarkably, cell size did not decrease as GIP increased (Fig. 1b), even though cells became highly deformed (Inset Fig. 1b). We posited that the deformation of cells was due to compressive forces originating from an increase in the concentration of intracellular osmolytes. Strikingly, we also observed a strong reduction in nuclear volume (Fig. 1c) and, as a result, the nuclear-to-cell volume ratio was perturbed (SI Fig. 1). Therefore, we concluded that the concentration of cytoplasmic macromolecules was also increasing. Surprisingly, our data were best fit assuming that these two osmolytes (small osmolytes and macromolecules) were increasing proportionally. Assuming that nuclear osmolarity did not adapt, we predicted that the nuclear volume v n would decrease with GIP, denoted P, as:

δ v n v n = - P/Π 0 1 + P/Π 0 (1)
where Π 0 is the intracellular nominal (P = 0 MPa) osmotic pressure and P corresponds to the surplus internal osmotic pressure above Π 0 . Fitting to this model yielded Π 0 ∼ 0.95 ± 0.05 MPa (dashed line Fig. 1c). We measured the osmotic pressure of the culture medium at 30 o C to be Π e ∼ 0.63 MPa, leading to a nominal osmotic pressure difference between the cell interior and the cell exterior ∆Π ∼ 0.3 MPa, in agreement with values from the literature [START_REF] Im De Mara On | Passive response of saccharomyces cerevisiae to osmotic shifts: cell volume variations depending on the physiological state[END_REF]. Since macromolecule concentration was increasing while cell volume remained constant, we predicted that macromolecular crowding would increase under GIP.

Changes in macromolecular crowding can be inferred by particle tracking microrheology [START_REF] Delarue | mtorc1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding[END_REF]. We recently developed genetically-encoded multimeric (GEM) nanoparticles as highly efficient tracer particles for microrheology [START_REF] Delarue | mtorc1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding[END_REF]. Introduction of a gene that encodes a self-assembling scaffold protein tagged with a fluorescent protein generates cells that constitutively contain tracer particles of defined sizes. In this study, we used GEMs of 20 nm (20nm-GEMs) and 40 nm (40-nm-GEMs) diameter. These particles probe the mesoscale, the length-scale of multimeric macromolecular assemblies such as RNA polymerase and ribosomes.

Using probes of various sizes, we found that the increase in cytoplasmic crowding under mechanical compression depended strongly on length-scale (Fig. 1d): the effective diffusion of larger particles such as mRNA (∼ 80 nm diameter [START_REF] Borodavka | Sizes of long rna molecules are determined by the branching patterns of their secondary structures[END_REF]), decreased far more than that of smaller particles such as 20nm-GEMs. We also found that the diffusion of a DNA locus decreased with GIP, probably as a consequence of decreased nuclear volume leading to increased nuclear crowding. Interestingly, the diffusivity of every tracer particle followed a similar exponential trend: Where D 0 is the nominal diffusion of each particle, P is the growth induced pressure (GIP), and P c is the characteristic pressure of the exponential dependence on GIP for each particle. This exponential dependence of diffusion on GIP is theoretically predicted from the Doolittle relationship, as previously described [START_REF] Delarue | mtorc1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding[END_REF] (see Modeling in SI). However, as above, this prediction only applies if osmolytes and macromolecules maintain a fixed, proportional concentration. We found that P c ∝ Π i /ζ , where ζ is a constant related to the interactions of the nanoparticle with its surroundings. Using osmotic perturbations to instantaneously modify crowding (SI Fig. 2), we were able to measure P c ∼ 0.6 MPa for 40nm-GEMs. Using this value our theory predicts well our empirical data (solid black line in Fig. 1d is the prediction, red dots are the data).

D = D 0 e -P/P c (2)
Experimentally, we found that the characteristic pressure P c depended on particle size, and was inversely proportional to the probe size s (1/P c = β s where β is the proportionality constant, 1e). This inverse relation implies that the effective cytosolic diffusion for a particle of any size, s (in nm) is a power law function of the diffusion at 40 nm: s ∝ e -β sP = e -β sP * 40/40 = e -β * 40 * P s/40 ∝ D 40nm s/40 . Using this relationship, we can predict cytosolic diffusivity at any length-scale from the effective diffusion of 40nm-GEMs (D 40nm ).

Our data show that confined growth leads to a concomitant increase in both internal osmotic pressure (leading to GIP) and macromolecular crowding (as evidenced by nuclear compaction and decreased nanoparticle diffusivity). Theory successfully predicts these observations if the increase in GIP and crowding are proportional. Another prediction of this proportional coupling is that relaxation of mechanical stress should lead to a cell volume increase proportional to GIP (see Modeling), δ v/v = P/Π 0 ; and for macromolecular crowding (and thus diffusivity) to reset to the nominal value without GIP. To test this prediction, we used a device in which GIP could be quickly relaxed. Consistent with our model, we observed a fast, fully reversible and predictable increase in cell volume, and recovery of GEM diffusion upon instantaneous relaxation of GIP (Fig. 1f). Thus, confined growth leads to a proportional increase in osmolyte and macromolecule concentration.

We next sought to investigate how GIP affects cell growth and protein production (which is dependent on the rates of multiple biochemical reactions). We first measured changes in cell number and chamber volume to estimate the cellular growth rate (Methods, SI Fig. 3). We observed that growth rate decreased roughly exponentially with GIP (Fig. 2a). To get insight into protein production, we used a fluorescent reporter assay. Protein production can take hours, raising the problem that GIP would continue to increase during the experiment if growth continued. To avoid this issue, we expressed the mCherry fluorescence protein from the ADH2 promoter (P ADH2 -mCherry) as our model system. The ADH2 promoter is activated by glucose starvation, a condition that also arrests cell growth [START_REF] Busti | Glucose signaling-mediated coordination of cell growth and cell cycle in saccharomyces cerevisiae[END_REF]. Thus, we could grow cells to develop a defined amount of GIP and then induce P ADH2 -mCherry by withdrawal of glucose (osmotically balancing with sorbitol) at a range of GIP values. In this way, we could infer how protein expression, at least of this model gene, was affected by GIP. We observed that the induction of the fluorescence signal was slower under GIP than in the control (Fig. 2b).

This experimental strategy enabled us to extract single-cell P ADH2 -mCherry fluorescence intensity curves. We observed that, after an initial time delay, which could be associated with sensing of carbon starvation or promoter remodeling, fluorescence intensity increased with time, and that this rate of increase was lower in compressed cells. We developed a simple mathematical model of transcription followed by translation to quantify induction of fluorescence. Our model predicted that protein concentration should increase quadratically with time, with an effective rate k exp that is the product of the transcription rate (k m ) and the translation rate (k p ): k exp = k m * k p (see Modeling). Although k exp was not stricto sensu a rate, but rather the product of two rates, we refer to it as a single effective rate hereafter, for the sake of simplicity.

Our simple model yielded an excellent fit to the experimental data (Fig. 2c), and enabled us to extract both the time delay and k exp . We observed that the time delay was progressively shorter with GIP (SI Fig. 4). It has previously been shown that cells in the G1 phase of the cell cycle respond more rapidly to stress [START_REF] Busti | Glucose signaling-mediated coordination of cell growth and cell cycle in saccharomyces cerevisiae[END_REF], and our previous studies showed that S. cerevisiae arrests in G1 in response to GIP [START_REF] Delarue | Self-driven jamming in growing microbial populations[END_REF][START_REF] Delarue | A simple molecular mechanism explains multiple patterns of cell-size regulation[END_REF]. Therefore, accumulation of cells in G1 could explain this reduced lag-time under GIP. We found that k exp decreased roughly exponentially with GIP (Fig. 2d), with a similar dependence as the growth rate decay (about 60% decrease at P = 0.3 MPa in both cases).

Our microrheology data and nuclear compression demonstrated that macromolecular crowding increased under GIP. We hypothesized that this crowding could limit protein expression rate and ultimately growth itself. This feedback could be physical as a result of decreases in the rate of diffusion-limited processes, with no need for specific signaling pathways. To test this idea, we set out to perturb molecular crowding by orthogonal means, using osmotic compression. HOG1 is a key kinase in the osmotic stress pathway. hog1∆ mutant cells cannot actively regulate their osmolarity [START_REF] Markus | The osmotic stress response of saccharomyces cerevisiae[END_REF], allowing us to maintain osmotic compression for sufficient time to assess growth and protein expression rates. Consistent with our hypothesis, we found that k exp of P ADH2 -mCherry decreased with osmotic compression (Fig. 3a).

GIP and osmotic compression are orthogonal means of increasing molecular crowding and activate distinct stress response pathways. Furthermore, the osmotic stress response is largely abrogated in hog1∆ cells [START_REF] Markus | The osmotic stress response of saccharomyces cerevisiae[END_REF]. If the effective expression rate (k exp ) of P ADH2 -mCherry is modulated by macromolecular crowding, then k exp should display the same relationship to the effective diffusion of 40nm-GEMs (D 40nm ) under both GIP and osmotic compression. Indeed, we observed the same dependence in both conditions (Fig. 3b) supporting the hypothesis that macromolecular crowding limits protein expression.

Our results are consistent with effective protein expression rate being diffusion-limited at a certain unknown length-scale, s. We found that the relationship between effective diffusion and particles diameter was a power law (Fig 1e). If crowding decreases P ADH2 -mCherry production by inhibiting diffusion of a rate-limiting particle, effective expression rate should be a power law function of D 40nm with an exponent that is the ratio of the particle size, s (in nm), divided by the size of 40nm-GEMs (i.e. 40 nm): Figure 3 Protein production and growth are diffusion limited processes. a. Effective expression rate (k exp ) of P ADH2 -mCherry decreases under osmotic stress in hog1∆ cells. b. k exp is a power-law function of the effective diffusion of 40nm-GEMs. c. Growth rate is proportional to protein production rate for hog1∆ cells under osmotic stress and WT cells under GIP. In all data, values are mean ± standard error of the mean, over N ≥ 3 independent biological replicates. Indeed, we observed this power-law dependence, with an exponent that suggests that expression was limited by the diffusion of particles of a characteristic size s ∼ 90 nm (Fig 3b). This mesoscale length-scale corresponds to many biological entities, for example trafficking vesicles and mRNA ribonucleoprotein particles [START_REF] Borodavka | Sizes of long rna molecules are determined by the branching patterns of their secondary structures[END_REF][START_REF] Elias M Puchner | Counting molecules in single organelles with superresolution microscopy allows tracking of the endosome maturation trajectory[END_REF][START_REF] Takamori | Molecular anatomy of a trafficking organelle[END_REF] (both ∼ 100 nm).

k exp ∝ D s ∝ D 40nm s/40
We next investigated the hypothesis that growth rate is mainly limited by protein expression. We plotted growth rate as a function of the effective expression rate of P ADH2 -mCherry and found that the two rates were roughly proportional. Note, this model gene is not limiting growth-rate, ADH2 is not expressed in the presence of glucose. Nevertheless, the fundamental processes required for its expression (e.g. transcription by RNA polymerase II and translation by ribosomes) are shared by all proteins. Consistent with the hypothesis that crowding limits both protein expression and cell growth, exactly the same relationship held for both osmotic compression of hog1∆ cells and WT cells under GIP (Fig. 3c).

Taking all of our results together, we developed a model of confined growth, with all parameters experimentally determined, allowing us to predict protein production and cell growth in confined conditions. The model derivation and parameterization are detailed in the Supplementary Information.

Our data support a central hypothesis that osmolyte and macromolecule production rates are tightly coupled. Exactly how this balance of rates is achieved remains unknown and is a longstanding fundamental question, but as a consequence, in the absence of confinement, cells grow and accumulate biomass while maintaining a constant level of macromolecular crowding. The accumulation of osmolytes increases osmotic pressure. The mechanical balance between osmotic pressure and the elastic properties of the cell wall in turn defines the turgor pressure [START_REF] Vella | The indentation of pressurized elastic shells: from polymeric capsules to yeast cells[END_REF]. This turgor pressure enables the cell wall to expand through a process of hydrolysis and insertion of new cell wall material [START_REF] Enrique R Rojas | Homeostatic cell growth is accomplished mechanically through membrane tension inhibition of cell-wall synthesis[END_REF][START_REF] Albersheim | Plant cell walls[END_REF]. We posit that insertion of cell wall material is only possible when the turgor pressure resulting from osmolyte accumulation is above a fixed value.

If the elasticity of the cell wall were to increase (i.e. require more force to be deformed), a higher pressure-difference, and thus more osmolytes, would be required to achieve expansion. This is also the case during confined growth where the surroundings mechanically resist cell growth. Confined growth leads to an effective increase in the elasticity around the cell which then physically limits cell wall expansion (Fig. 4a). In our experiments, when cells fill the confining chamber and start to distort one another and the chamber walls, they experience an effective surrounding elasticity, E eq . When cells grow by δ v, they need to accumulate more osmolytes to expand the cell wall, resulting in an increased internal pressure, which is the product of the surrounding elasticity and the volume change: E eq * δ v/v. This value is the growth-induced pressure (GIP). Based on our central hypothesis that the accumulation of osmolytes is proportionally coupled to the accumulation of macromolecular biomass, the decreased expansion rate will lead to increased crowding. This increase in crowding then feeds back onto both protein and osmolyte production, which further reduces the cell expansion rate (Fig 4a).

We calibrated the parameters related to our confining growth model, including the value of the turgor pressure, using laser ablation, transmission electron microscopy and atomic force microscopy (Supplementary Information).

Our experimentally calibrated model accurately predicted the dependence of protein production and cell growth rate on pressure, as well as the dynamics of confined cell proliferation and GIP buildup, without any fitting of free parameters (thick orange lines in Figs. 4b-d). This remarkable predictive power supports our simple model: Growth is initially limited by the surrounding elastic environment, which forces the cell to increase internal osmolarity. Osmolyte production is directly coupled to macromolecular biosynthesis thus leading to mesoscale crowding. High mesoscale intracellular crowding then physically inhibits reactions through diffusion-limited processes. Our model shows that most of the observed decrease in growth rate can be explained by this physical feedback, without the need to evoke any other mechanism.

We also investigated the predictions of our model if we removed the physical feedback (thick red lines in Figs. 4bd). In this case, GIP and cell number would rise much more quickly than experimentally observed. Growth would still ultimately decrease, due to the increasing mechanical barrier to cell expansion, but much more slowly than observed because the rate of osmolyte production would not be limited. In this case, crowding would also rise quickly, and crowders in the cell would approach the maximum random close packing fraction much sooner. We speculate that the physical feedback of crowding on biosynthesis is adaptive, as it delays and attenuates macromolecular overcrowding, which could allow more time for stress responses to more efficiently activate. An intriguing question is why cells have not evolved adaptive mechanisms to change the relative rates of macromolecular biosynthesis and osmolyte production to prevent overcrowding of the cell. The osmotic stress response is an adaptive mechanism of this type. However, we observed that GIP in hog1∆ mutants, which are defective for the osmotic stress pathway, was similar to that in wild-type cells (SI Fig. 5). A key difference between GIP and osmotic shock is the effect on turgor. The activation of the osmoadaptive HOG1 pathway in S. cerevisiae is linked with a loss of turgor [START_REF] Schaber | Biophysical properties of saccharomyces cerevisiae and their relationship with hog pathway activation[END_REF]. However, our results suggest that turgor does not decrease during GIP, and in fact increases due to the elasticity of the surroundings effectively acting like a thicker cell wall. Increased turgor actually triggers the hypoosmotic stress response, which decreases intracellular osmolarity and subsequently cell volume [START_REF] David E Levin | Cell wall integrity signaling in saccharomyces cerevisiae[END_REF]. However, this would be counterproductive during confined growth as reduced cell volume would further increase crowding. Indeed, pathways related to the response to both hyper-and hypo-osmotic stress are triggered by GIP. These pathways, which together constitute the SCWISh network [14], are important for cell survival under GIP, but they do not appear to change the coupling between osmolyte and macromolecule biosynthesis. Perhaps the feedback between mesoscale crowding and growth is useful: diffusion is affected with a strong size-dependence, mainly limiting reactions at the mesoscale (≥ 10 nm diameter). It is intriguing that many stress response proteins are relatively small. Therefore, upon developing strong growth-induced pressure, growth will stall, but stress-response pathways can continue to operate.

Stress-response signaling pathways vary extensively between organisms. In contrast, high macromolecular crowding is a fundamental property of all life forms [START_REF] John | Macromolecular obvious but underappreciated[END_REF]. Our results suggest that a primordial biophysical feedback mechanism arises directly from the physical properties of cells.

Figure 1

 1 Figure 1 Confined growth leads to the intracellular accumulation of osmolytes and macromolecules. a. Confined growth leads to the build-up of growth-induced pressure (GIP), measured by the deformation of the PDMS chamber. b. Cell volume distribution under GIP. Insets: representative 3D reconstructions of a non-compressed cell and a cell at 0.4 MPa. Both cells have a similar volume ∼ 65 fL. c. Nuclear volume decreases under GIP. Dashed line: fit of nuclear volume as a function of GIP assuming constant nuclear osmotic pressure. d. Diffusivities of various particles and a DNA locus all decrease exponentially as a function of GIP. Solid black curve is the model prediction for 40nm-GEMs. e. The characteristic pressure(P c) of the exponential dependence is inversely proportional to cytosolic particle size. f. After sudden pressure relaxation, effective diffusion rises quickly (< 1 minute) to control (uncompressed) values and cell volume increases (δ v) due to stored osmotic pressure. Predicted values are indicated. In all data, values are mean ± standard error of the mean, N ≥ 3 independent biological replicates.

Figure 2

 2 Figure 2 Confinement decreases growth and protein production rates. a. Growth rate decays roughly exponentially with GIP. b.Representative images from protein production reporter system. A reporter gene consisting of the mCherry fluorescent protein under the control of the ADH2 promoter was integrated at the endogenous locus; glucose starvation induces the gene. After 7 h of induction, we observe stronger induction for control (no pressure, left) condition than under GIP (right). c. Single cell fluorescence intensities were fit with a quadratic function to extract an effective expression rate k exp at various values of GIP. Representative curves for a single cell is shown with fitting; multiple single cell traces are shown inset. d. Protein expression rate decreases roughly exponentially with GIP. In all data, values are mean ± standard error of the mean, over n ≥ 100 cells in N ≥ 3 independent biological replicates.

Figure 4 A

 4 Figure 4 A physical feedback model, in which crowding limits protein production, predicts the dynamics of confined cell growth. a. Schematic of the model. After confluence is reached, growth induced pressure (GIP) increases as a function of cell volume change and the elasticity of the surrounding cells and PDMS chamber walls. Cells must accumulate more osmolytes to grow in the face of increasing effective elasticity, therefore volume expansion is inhibited. Macromolecule biogenesis is proportionally coupled to osmolyte production and so intracellular crowding increases. Increased mesoscale crowding feeds back onto many processes including the processes associated with macromolecule biogenesis itself, thereby limiting growth. b-e. Predictions of the dependence of various observables on GIP from the model. All parameters are experimentally determined. Predictions are shown for: effective protein expression rate (k exp , b); growth rate (c); cell number (d); and GIP (e). In all plots, the thick orange line represents the model prediction. The thick red line represents the prediction of the model without any physical (crowding) feedback on biomass production. The dashed line represents the onset of confluence and GIP buildup. The R 2 value indicates the square difference of the model against the data. Values are mean ± standard error of the mean; N ≥ 3 independent biological replicates.

Acknowledgments

We thank E. Kassianidou for help with laser ablation experiments. We thank NYULH DART Microscopy Laboratory Alice Liang, Chris Petzold and Kristen Dancel-Manning for consultation and assistance with TEM work; this core is partially funded by NYU Cancer Center Support Grant NIH/NCI P30CA016087. The technological realisations and associated research works were partly supported by the French RENAT-ECH network. LJH was funded by NIH grants R01 GM132447 and R37 CA240765. We thank E. Rojas for fruitful discussions. LJH and MD thank the FACE foundation for travel support.

Author Contributions

BA and MD designed and performed the experiments and data analysis; CCD and ED performed the AFM experiments; LJH designed the strains used in the study; BA, LJH and MD wrote the manuscript.


Tessa Beinema
email: t.c.beinema@utwente.nl

Daniel Davison
email: d.p.davison@utwente.nl

Dennis Reidsma
email: d.reidsma@utwente.nl

Oresti Banos

Merijn Bruijnes

Brice Donval

Álvaro Fides Valero

Dirk Heylen

Dennis Hofs

Gerwin Huizing

Reshamashree B Kantharaju

Randy Klaassen

Jan Kolkmeier

Kostas Konsolakis

Alison Pease

Catherine Pelachaud

Donatella Simonetti

Mark Snaith

Vicente Traver

Jorien Van Loon

Jacky Visser

Marcel Weusthof

Fajrian Yunus

Hermie Hermens

Harm Op Den Akker

Merijn Brui- Jnes

Reshmashree B Kantharaju

Agents United: An Open Platform for Multi-Agent Conversational Systems

Keywords: Human-centered computing → Natural language interfaces; Systems Open source platform, multi-agent system, social conversational agents, coaching

Figure 1: A group of ASAP (1, 3, 5) and GRETA (2, 4) agents in the same Unity environment. A user can participate in a multiparty group conversation by selecting a response from the menu on the right (in the same interface or on a separate device).

INTRODUCTION

Developing intelligent virtual agent (IVA) systems can require complex integration of many state-of-the-art technical components, especially when developing for long-term social interactions with multiple virtual agents. This article presents the Agents United Platform 1 , a novel open source platform that provides researchers and developers with an integrated setup for the creation of long-term multi-IVA applications.

The platform was the result of technical development during the Council of Coaches project [START_REF] Op Den Akker | Council of Coaches: A novel holistic behavior change coaching approach[END_REF]. In this project, a health coaching application was built that offers coaching by a group of virtual agents. Users could engage in multi-party group discussions with these agents, receiving advice and information on various aspects of healthy living (also see Figure 1). Each agent in this council had their own personality, backstory, and expertise (e.g., physical activity or nutrition). Throughout this paper, we will provide examples in the context of this health coaching use case to illustrate the discussed concepts and technical explanations 2 . The application was built on a number of state-of-the-art components from the field of intelligent virtual humans and dialog systems and turned into a versatile, reusable and standalone platform. To support and coordinate the activities with respect to the platform, such as maintenance, future development, community building and documentation, the Agents United Alliance has been formed. This paper presents the platform. We begin this article with an overview of its global architecture, followed by a discussion of its components and the advances made on individual components. Next, we discuss contributions through the integration of components, and conclude with expected future developments and directions for the platform.

THE AGENTS UNITED ARCHITECTURE

The architecture for the Agents United Platform follows the wellknown sense, remember, think and act paradigm (see Figure 2) for intelligent systems that has been used for decades. The sense layer contains sensing and interpretation of human behaviour and health related parameters, the outcomes of which is stored in a central Shared Knowledge Base (the remember layer). The think layer contains the interaction management, such as management of coaching topics, content, and the social dialogue used to deliver the content. The act layer contains the virtual embodiment of the agents as well as the user GUI (for presenting response options to the user and for presentation of additional information) and the Wizard of Oz GUI (for presenting the controls with which the Wizard can select specific agent moves in experiments). As indicated in Figure 2, components in the architecture are or can be distributed over various types of devices. Communication with components on servers is performed through RESTful interaction, while communication between components on the experimenter's device communicate over an ActiveMQ middleware layer 3 .

While individual components are presented in detail in their respective publications, the following subsections provide a summary of each component, and its novelty and role in the platform.

Holistic Behaviour Analysis Framework (HBAF): From Sensing to Interpreting Users' Behaviour

A virtual agent system should take users' personal situation into account during interactions, for example, by using sensor data collected in the users' daily life. For a coaching application as developed in the Council of Coaches project, a valuable starting point for the conversations is the users' behaviour. Ideally, for a health coaching application an holistic profile would be available. That is, a broad picture of the user as a whole, that includes behavioural aspects on multiple domains. In our use case, domains for which behaviours were measured included physical activity and social interaction. This information was gathered using a smartphone, wearable, a weight scale and a blood pressure measuring device. The Holistic Behaviour Analysis Framework (HBAF) focuses on sensing and profiling user's context by combining smart multimodal sensing technologies in order to model users' behaviour in a comprehensive way [START_REF] Konsolakis | A novel framework for the holistic monitoring and analysis of human behaviour[END_REF]. More specifically, the framework serves as the principal generator of knowledge on a user's behaviour over different periods of time through modelling, inferring and combining human behaviour in a holistic way, including physical, social, emotional and cognitive domains [START_REF] Konsolakis | Human behaviour analysis through smartphones[END_REF].

The HBAF deals with heterogeneous data from multi-modal devices, such as smartphones, activity trackers and UniversAALenabled devices [START_REF] Tazari | The universAAL Reference Model for AAL[END_REF] -a standard that was originally co-developed by one of the Alliance partners and previously applied by them as input paradigm in healthy living scenarios [START_REF] Pace | IoT platforms interoperability for active and assisted living healthcare services support[END_REF]. In the COUCH demonstrator, UniversAAL connects a weight scale and a blood pressure measuring device, but it also supports a variety of smart home devices. This data is securely gathered, after which selected views and summaries are generated. Only this 'derived content' is accessible for the rest of the platform, ensuring that users' sensitive data are exclusively stored in the HBAF. The HBAF detects the user's physical (e.g., walking), social (e.g., having a conversation), emotional (e.g., feeling happy), and cognitive (e.g., reading a book) behaviours. These classes of information have been selected for their potential to ground opportunities for behaviour change through coaching conversations, although the HBAF's architecture is not necessarily limited to those classes. Furthermore, behavioural data is analysed at different time scales, affording the detection of short-term behaviours (e.g., sitting on a chair), long-term behaviours (e.g., being sedentary) and changes in these behaviours over time.

WOOL Web Service: Shared Knowledge Base With Access Authorisation

The WOOL Web Service 4 fulfils the role of the Shared Knowledge base (SKB) in the Agents United Platform. As the central 'memory' of the system it stores all necessary information and makes it accessible to all other modules in the platform. This does not only involve immediate memory (at the level of the ongoing conversation), but also longer term memory of sensor data, personal characteristics, coaching goals, and a conversational history per user that spans across recurring interactions. In our use case, a naming scheme for variables is used to store information and distinguish between information that should be remembered between dialogues (e.g., dialogues that have been completed or a goal that has been set) and information that is stored 4 The Web Service is part of the WOOL Dialogue Platform: www.woolplatform.eu and used during execution of a dialogue (e.g., which paths through the current dialogue have been completed). Variables in the last case will be overwritten when the dialogue is held again, whereas other data is retained longer term; also some variables are stored as histories or time series data whereas for other variables only the most recent value is retained.

Other examples of such a database exist, there are many more agent applications with some kind of memory storage (e.g., [START_REF] Elvir | Remembering a Conversation -A Conversational Memory Architecture for Embodied Conversational Agents[END_REF][START_REF] Richards | ForgetMeNot: What and how users expect intelligent virtual agents to recall and forget personal conversational content[END_REF]), but together with the middleware communication protocols employed between components in the Agents United Platform, the SKB is crucial for a fluent information flow between system components, which can be facilitated by a consistent naming scheme.

The Topic Selection Engine

The Topic Selection Engine (TSE) decides which topic is the subject of the conversation that is to be started, which allows for tailoring the conversational (coaching) strategy and content on a high level (following [START_REF] Beinema | Creating an artificial coaching engine for multi-domain conversational coaches in eHealth applications[END_REF]). This way, the TSE ensures that the agents will discuss a relevant topic with the user. For example, in our use case, the coaches will introduce themselves to the user before they move on to coaching topics such as setting a new physical activity goal.

The engine uses topic trees (one for each conversational agent) that embody a taxonomy of the conversational topics. In our use case, each agent is the expert on one coaching domain (e.g., physical activity or nutrition) and the TSE tree for an agent includes topics that can be subject of a coaching dialogue for that agent's domain (e.g., introduction, goal-setting, or provide information). Topics in the tree may be more or less relevant during the interaction, based on available information such as users' current status (e.g., from sensor data), interaction history (e.g., previously addressed topics and conversation outcomes), and preferred coaching strategy (e.g. from the set of strategies in [START_REF] Beinema | Tailoring coaching strategies to users' motivation in a multi-agent health coaching application[END_REF]). Examples of rules to compute the relevance of a topic are conversational (e.g., 'start with an introduction') or task related (e.g., 'discuss goal-setting' when no goal has been set yet or 'increase relevance for inform topics' when the strategy of the agent is health education). Selection of the most relevant topic uses the weighted average of selection parameters that are added to each topic. These parameters represent partially predetermined information ('how relevant is discussion of the topic') and their values are adapted dynamically based on available information (all retrieved from the SKB).

Upon request by the interaction manager (see Section 2.4) the TSE selects the most salient topic from the tree (as shown in Figure 3). It returns a topic message which contains an indication of the general topic (e.g., 'goal-setting'), information about the speakers involved and parameters specifying content (e.g., 'a preferred goal would be about a new daily step count target'). This information is used by other modules in the think layer to generate actual dialogue.

This TSE is one of the first mechanisms that allows automated multi-topic coaching, where the advice and delivery can be made dynamic, personalised, and sensor based, which are particularly relevant aspects for our use case of health coaching. State-of-the-art work [START_REF] Beun | Talk and tools: The best of both worlds in mobile user interfaces for e-coaching[END_REF][START_REF] Cafaro | Selecting and expressing communicative functions in a SAIBAcompliant agent framework[END_REF][START_REF] Sidner | Creating new technologies for companionable agents to support isolated older adults[END_REF] tends either to focus on how to present coaching dialogues (with a set order of topics), or to be more about the notification-style delivery of pre-defined coaching content (through text messages or smartphone notifications) [START_REF] Vries | A word of advice: How to tailor motivational text messages based on behavior change theory to personality and gender[END_REF][START_REF] Op Den Akker | Tailoring real-time physical activity coaching systems: A literature survey and model[END_REF].

Flipper: Interaction Management and Conversational Intent Planning

The Interaction Manager is the conversationally-aware glue between various components, providing the 'beat' to which the other components in the think layer run. The Interaction Manager has been built using the Flipper system [START_REF] Maat | Flipper: An information state component for spoken dialogue systems[END_REF][START_REF] Van Waterschoot | Flipper 2.0: A pragmatic dialogue engine for embodied conversational agents[END_REF], an information state based conversation engine that choreographs other modules involved in producing the conversation. Addressing a single topic can involve a complex dialogue of moves, and a single 'move' utterance might require multiple social conversational moves (including turntaking and back-channelling), to be delivered properly. Flipper is used to orchestrate this process and, while doing so, keeps track of when moves have been delivered, whether they were interrupted, or not performed. In our use case, for example, when setting a physical activity goal, we want to know when certain coaching information is delivered and when the user has agreed on a certain goal (e.g. '7,500 steps'). This process of negotiating a suitable goal requires choreographed conversational moves between an activity coach and a social coach. The latter may respond to the activity coach with statements such as "Group activities are a great way to motivate you to reach your physical activity goal. " As interaction manager, Flipper communicates with most other modules as illustrated in Figure 2: Flipper (1) requests, from the TSE, new topics to be addressed; (2) controls the dialogue engine to successively determine possible next conversational moves and generate corresponding multi-modal utterances using information from the SKB; (3) requests the SAIBA behaviour realisers to produce selected utterances on the multi-agent embodiments; (4) receives user responses via the GUI, which it communicates back to the dialogue engine. This process allows for a separate definition of structural (dialogue) content and social conversation turn-taking management, which is a novelty of the integrated platform on which we will elaborate in Section 3.1.

WOOL & DAF: Dialogue Modelling and Execution Engines

The Agents United Platform features two variants of dialogue execution engines: WOOL, which models dialogues as plain transition graphs, and the state-of-the-art Dialogue and Argumentation Framework (DAF), which models dialogues as structured argumentation. In our use case, scripted linear dialogues (such as introductions or casual conversations) were modelled with WOOL, whereas dynamic dialogues (such as setting and reflecting on an activity goal) made use of argumentation and reasoning offered by the DAF. The engines model the structure of dialogues that cover a topic (as selected by the TSE); and for every step in the dialogue they generate an appropriate description of a next utterance that one of the agents can deliver and a set of possible responses for the user. Determination of a next dialogue step and the content of utterances may be influenced by information in the SKB. A dialogue progresses to a next utterance whenever the interaction manager requests the next dialogue step (e.g., because it received a user response). The execution engines can also return instructions to the interaction manager to store information in the SKB upon completion of a specific move (e.g., 'this dialogue has been completed' or 'such-andsuch goal has been set').

An overall novelty of these integrated execution engines is that their dialogues are implicitly modelled as being part of a larger topic structure that is navigated by the TSE, and the interaction manager can switch between dialogues (and engines) during the overall conversation.

2.5.1 WOOL: Labelled transition graphs as dialogue content. WOOL5 is built as an adaptation of the Yarn language 6 and models dialogues as transition graphs that link agent statements and user replies. This dialogue engine and language provide a lightweight way to, for example, include an initial version of dialogues for testing, or to include single use dialogues (such as the scripted first introductions in our use case). The editor supports the dialogue development process through an intuitive GUI for authoring and testing dialogues. Once a dialogue has been started with the user, the user's response to a statement by an agent determines what the next agent-statement should be. This process is repeated until the end of the dialogue has been reached.

Dialogue and Argumentation

Framework: Argument driven dialogues. The Dialogue and Argumentation Framework (DAF) (an implementation of the Platform of Argument and Dialogue [START_REF] Snaith | A modular platform for argument and dialogue[END_REF]) has been built on top of the Dialogue Game Execution Platform (DGEP) [START_REF] Bex | Generalising argument dialogue with the Dialogue Game Execution Platform[END_REF] and the Dialogue Utterance Generator (DUG) [START_REF] Snaith | An argument-based framework for selecting dialogue move types and content[END_REF]. DGEP is used to manage the dialogue through dialogue games that define and regulate on an abstract level how a dialogue can unfold. These dialogue games are specified in the Dialogue Game Description Language (DGDL) [START_REF] Lawrence | Debating technology for dialogical argument: Sensemaking, engagement and analytics[END_REF][START_REF] Wells | A domain specific language for describing diverse systems of dialogue[END_REF] and describe in terms of dialogue acts which types of move are permitted and their allowed order (e.g., a response to a 'question' can be an 'answer' or another 'question'). In the Agents United Platform, DGEP has been initialised with models of the abstract structure of coaching (sub)dialogues (for an example see the 'goal-setting' dialogue game discussed in [START_REF] Snaith | A dialogue game for multi-party goal-setting in health coaching[END_REF]). These dialogue games model dialogues in terms of move types that can follow each other while discussing a specific type of topic. Move types can be, for instance, question, answer, argument, counterargument, propose, accept and elaboration. Dialogue games may be different for (sub)topics as we learned from analysing conversations between multiple health professionals and patient-actors (the Patient Consultation Corpus [START_REF] Snaith | A multimodal corpus of simulated consultations between a patient and multiple healthcare professionals[END_REF]). Upon request by the interaction manager, DGEP will initiate one of these dialogue games, and successively Figure 4: An example of two dictionary entries in the DUG. Note that the terms between double curly brackets are filled by content from the SKB (in this case 'short-term' or 'longterm') and that '$X' is replaced by a value assigned in the logical content (e.g., 10,000 steps).

offer a list of all potential 'next moves in the game' for each of the agents in the conversation as well as for the user response options.

The abstract moves returned by DGEP need to be filled with content. For example, an abstract 'propose a new goal' move requires knowledge of what type of goal is to be proposed, and the words of the utterances (i.e., there are many ways to phrase a proposal for a goal of '1000 more steps per day'). Filling in the moves is done by the Dialogue Utterance Generator (DUG). The DUG instantiates the moves with content and filters them based on strategic and other factors. It has two components: argument models (defined using ASPIC+ [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF]) and a dictionary. The argument models use inference rules to deduce possible move content (using information from the SKB), and the dictionary then maps these logical representations to utterances. For example, in our use case, the expression 𝑠𝑒𝑡_𝑠𝑡𝑒𝑝_𝑔𝑜𝑎𝑙 (10, 000) can be presented to the user as 'How about we set you a step goal of 10,000 steps?' or 'A step goal of 10,000 steps would be really good for you -what do you think?'. Utterances in the dictionary are marked-up with various annotations that can be used to select a suitable phrasing (e.g., to represent delivery styles such as 'friendly' or 'authoritative'; or content such as 'proposing a goal' or 'about step goals'; see Figure 4). The DUG also fills in slots in the utterances with user-specific SKB data (e.g., the user's name, or the last amount of steps that was measured).

GRETA & ASAP: Multi-Modal Behaviour Realisers

The setup of the Agents United Platform ensures that conversational content can be delivered via any SAIBA-compliant multi-modal behaviour realiser [START_REF] Kopp | Towards a common framework for multimodal generation: The behavior markup language[END_REF]. Currently, two embodied virtual agent platforms are fully integrated: GRETA [START_REF] Niewiadomski | Greta: An interactive expressive ECA system[END_REF] and ASAP [START_REF] Van Welbergen | AsapRealizer 2.0: The next steps in fluent behavior realization for ECAs[END_REF]. In our use case, various agents from both platforms (as also previously shown in Figure 1) delivered the coaching content in an integrated virtual environment, where agents held conversations between each other and the user.

2.6.1 Virtual Embodiments via the GRETA Platform. Of the two virtual agent platforms, GRETA [START_REF] Niewiadomski | Greta: An interactive expressive ECA system[END_REF] is the most advanced with respect to the autonomous nonverbal behaviour generation that accompanies agents' utterances. The GRETA agents' behaviours are driven at three different levels: first, by indicating its communicative intentions using the Function Markup Language FML-APML [START_REF] Mancini | The FML-APML language[END_REF]; second, from a semantic analysis of the agent's utterance [START_REF] Ravenet | Automatic nonverbal behavior generation from image schemas[END_REF]; and third, from the prosody [START_REF] Yunus | Sequence-to-Sequence Predictive Model: From Prosody To Communicative Gestures[END_REF]. The interaction manager (Flipper) outputs the communicative intentions. The other two methods then compute automatically where to place a communicative gesture. GRETA relies on the representation of image schemas [START_REF] Cienki | Image schemas and gesture[END_REF] to compute the shape of a gesture. Such a representation captures the conceptual meaning of a text in terms of objects and actions [START_REF] Cienki | Image schemas and gesture[END_REF] which are mapped to a physical gesture shape [START_REF] Ravenet | Automating the production of communicative gestures in embodied characters[END_REF]. Having computed the different multi-modal behaviours, GRETA relies on its behaviour scheduler to organise them sequentially [START_REF] Mancini | Distinctiveness in multimodal behaviors[END_REF]. Finally, it uses the notion of Ideational Units [START_REF] Calbris | Elements of meaning in gesture[END_REF] to co-articulate gestures within each other [START_REF] Xu | Compound gesture generation: A model based on ideational units[END_REF].

We adapted the open source GRETA platform in two ways, (1) allowing multiple agents in one environment, and (2) importing into Unity, which also allows for integration with ASAP (see Section 3.2).

The inclusion of multiple agents in one environment required agents to be added as independent entities. Therefore, we added a hierarchical structure to GRETA. At the highest level, we added an environment manager that knows about the objects and their location. It can contain various objects, in particular agents. Each agent is controlled by its own 'CharacterManager' module that contains its information: the 3D aspect, voice, behaviour lexicons and baseline [START_REF] Mancini | Distinctiveness in multimodal behaviors[END_REF]. Feedback mechanisms ensure the information flow between the components. They allow an agent to know where another agent is located and what it is doing.

Furthermore, we have integrated GRETA within Unity 7 . The integration was done using a TCP-IP message controller where Unity acts as the server and GRETA as the client. As soon as an animation has been computed within GRETA, it sends it frame by frame to Unity. Such an integration allows for the use of all graphic functionalities of Unity and the behaviour controller of GRETA.

Virtual

Embodiments via the ASAP Platform. The other virtual agent platform that we incorporated, ASAP, is a behaviour realiser with a strength for choreographing and realising multi-modal behaviours of multiple agents [START_REF] Van Welbergen | An incremental multimodal realizer for behavior co-articulation and coordination[END_REF][START_REF] Van Welbergen | Elckerlyc -A BML Realizer for continuous, multimodal interaction with a Virtual Human[END_REF][START_REF] Van Welbergen | AsapRealizer 2.0: The next steps in fluent behavior realization for ECAs[END_REF]. It supports visualisation in the Unity game engine [START_REF] Kolkmeier | An ASAP Realizer-Unity3D bridge for virtual and mixed reality applications[END_REF] and has been developed specifically for adaptive timing and mutual coordination of behaviour between multiple agents and between agent and user [START_REF] Reidsma | AsapRealizer in practice -A modular and extensible architecture for a BML Realizer[END_REF][START_REF] Van Welbergen | Multimodal plan representation for adaptable BML scheduling[END_REF].

We extended upon ASAP in two ways. Firstly, we integrated the feedback channels from GRETA into standard BML patterns which allows ASAP agents to seamlessly respond to GRETA agents. This is done in almost equally flexible and adaptive ways as was already present for ASAP [START_REF] Van Welbergen | Multimodal plan representation for adaptable BML scheduling[END_REF]. Secondly, integration with Unity was extended in a way that allows not only multiple ASAP agents on one device (as was already possible), but even a mix of agents (ASAP and GRETA) embodied on multiple devices. Since this is a novel integration between multiple components in the platform we discuss this in further detail in Section 3.2.

NOVELTY IN INTEGRATED PLATFORM

The Agents United Platform seamlessly integrates the previously discussed components. In the following subsections, we highlight some of the innovations that result from and are supported by this integration.

Mixing Structural and Social Models of Conversational Dialog

A first advance of the state-of-the-art by the platform lies in combining expertise, models, and insights from the fields of argumentation and social conversation. In the Agents United Platform, each component in the think layer expands a selected topic into more detailed steps. Ultimately the execution of a single utterance is orchestrated by the interaction manager, which may involve multiple social conversational moves that include turn-taking, back-channelling, and detection of completion or interruption. The novelty of this integration between components in the system is the way that they work together to move from models of coaching topics and content, to structural models of dialogue to determine the content of successive moves in the conversation, via models of social conversational intents that are needed to deliver a single piece of content, to actual delivery of the content in the form of agent behaviour. By giving the interaction manager the task of choosing among all possible next moves from multiple agents as determined by the dialogue model, the social conversational turn-taking management can be defined by a separate set of rulesthat is, separate from the content modelled by the dialogue engines and Topic Selection Engine. This not only allows for specialists to define those respective models and rules (e.g., by letting them focus on dialogue design or social behaviour), but also means that any additional device can be added to serve as an input for user responses or Wizard of Oz commands (e.g., to move the dialogue forward).

Multi-Platform Multi-Engine Multi-Device Multi-Agent Setups

A second advance of the state-of-the-art by the platform is that it allows for integrated use of both GRETA and ASAP, two platforms for realising agent behaviour. Agents from both platforms can be included in one Unity scene, but they can also be potentially spread out over any number of devices (as long as these devices support Unity) while remaining part of the same interaction. All agents in an interaction are driven by one single instance of the multi-agent ASAP planner and realiser, which makes crossdevice coordination between agents, user reply interfaces, and Wizard of Oz interfaces possible (see Figure 5). Thanks to the integrated intent planning and the fact that agents have access to information in each other's feedback channel, the agents of the two engines are aware of each other's presence in the conversation and are responding to each other; not just verbally, but also in their autonomous social conversational nonverbal cues (e.g., gaze).

Conversations With the Coach-as-a-Sensor

A third novel aspect of the integrated system is the potential to perform measurements and behaviour analysis using the 'Coachas-a-Sensor' concept. Some types of behaviour (such as emotional experiences) are difficult to accurately measure with sensors -especially unobtrusively in an daily life context. The setup of the integrated platform is ideally suited for in-dialogue measurements. While similar to the experience sampling methodology (where users receive notifications for questions), embedding questions in a natural conversation with an agent would make these measurements less obtrusive. The collected information is then stored in the SKB and can be retrieved by the HBAF (e.g., for use in behaviour analysis as in Wohlfahrt-Laymann et al. [START_REF] Wohlfahrt-Laymann | Enabling remote assessment of cognitive behaviour through mobile experience sampling[END_REF]). Dialogues can be designed specifically with this goal in mind, making use of questions for psycho-social assessment to support more complex analysis of the social and emotional state of users based on the combination of sensor data and self-reports through the Coach-as-a-Sensor.

While conversational systems typically have ways to retrieve and store information about the user as it arises through the interaction, the setup described here offers infrastructure that is geared towards interpreting such data on the sensing layer, closely embedding it in HBAF models and driving new insights.

Tailoring Interaction Strategies & Styles

A fourth aspect that is well supported by the integrated platform is the potential to tailor not just conversational content, but also the underlying interaction strategies and styles. For example, in our health coaching use case, discussing the relevant topics and providing tailored information from a content perspective are important. But the manner in which this content is discussed is also an important factor in keeping the user engaged and motivated to work on their behaviour. Adjusting the underlying coaching strategy (e.g., goal-setting or health education) and style (e.g., authoritative or socratic) is well supported by the integrated platform.

Whether these strategy and style choices are made implicitly (deduced by the system) or explicitly (chosen by the user or discussed between user and coach), the architecture can directly adapt the resulting interaction accordingly. Information on strategy and style can be stored in the SKB and shared between components. The Topic Selection Engine can use this information in its topic selection process and include parameters indicating delivery style preferences in its topic messages. This allows for the dialogue engines to either play the suitable scripts (WOOL) or to take these preferences into account during execution of the abstract dialogue games and utterance selection (DAF). Finally, the behaviour realisers are able to generate matching non-verbal behaviour.

FUTURE WORK

Given where the platform stands now, new questions arise. In this section we discuss a few directions for future work. A first direction for future work is in the further development of group-aware (semi-)autonomous nonverbal agent behaviours. We already implemented rudimentary automatic conversational gaze behaviour for the agents in the integrated platform. This gaze behaviour takes into account which agent is speaking and whom they are addressing. However, this behaviour does not take into account dialogue content, nor group dynamics including cohesion, leadership, relationship between participants, etc.; it just looks at the activity in the behaviour realiser feedback channels. In future work we could now investigate and further develop the generation of such group social behaviours. This could be in terms of non-verbal behaviour of individual agents in a group context, but also improved turn-taking management between agents.

A second direction would be to improve functionality to support the addition of new agents and content. In the integrated platform, topics are mapped to dialogues and specific agents that fulfil certain roles in those dialogues. Automating this process of casting agents for these roles would make the system more flexible. Future work could therefore focus on the development of a 'casting director' component for this purpose. Furthermore, it could investigate 'import functionality' for new agents with their own expertisefor example, from other developers through a marketplace. Such imported agents, or updates to content in general do come with their own challenges. You would want the content to be coherent, especially for use over a longer period of time and want to avoid scenarios in which the agent, for example, suddenly gives diametrically opposite advice compared to previous advice.

A third direction for future development would be more finegrained access authorisation. In the current setup of the platform, all components can access the Shared Knowledge Base. Even though the Holistic Behaviour Analysis Framework already provides a layer of data security by only sending on 'derived conclusions' instead of all raw data, not all stored information is relevant for all components. While currently all content and components are added by those developing an application, if a some point it becomes possible to import new content or agents this security will be important. Furthermore, this could also mean that a method for gathering and revising consent -for example, through conversations between the agents and the user -would have to be devised to support access to specific (sensor) data by specific components.

RELEASING THE PLATFORM AND FUTURE COMMUNITY BUILDING

The Agents United Platform has been released open source under a LGPL v3.0 license. The platform's website8 provides an overview and showcases the platform; the platform's Git repository 9 hosts the latest release and a demonstrator that showcases a potential application. It also contains documentation for the platform as a whole and its components, and tutorials10 that explain how to setup and develop your own application using the platform.

To support and coordinate the activities with respect to the platform, such as maintenance, future development, community building and documentation, the Agents United Alliance has been formed. While the website provides more details on the Alliance and the Platform, we will summarise some key points here.

The agenda for further development of the platform is set by members of the Alliance together with the community. Initially, members of the Alliance were the technical partners from the Council of Coaches project. However, since September 2020, two additional parties have already joined to actively contribute to the platform's development.

Members of the Alliance are committed to actively working on the documentation, tutorials and development of the platform, as well as keeping the platform up to date with new releases of its components. This work is coordinated by the Personalised eHealth Technology programme of the University of Twente and supported by ongoing projects that use the platform or its individual components.

CONCLUSION

In this article, we presented the Agents United Platform, an open source platform for multi-agent conversational systems. The platform integrates a set of state-of-the-art components, advancing the capabilities of existing components and introducing several new ones. This offers novel capabilities to the platform as a whole and presents interesting directions for future work.

Furthermore, the platform is not just a topic of research in itself, but it is also a vehicle for research. It facilitates easy setup of new agent applications, which in turn allows researchers and developers to focus their efforts on the areas of interest for their research. They can, for example, setup different kinds of user studies on how agents can influence and inform users. Instead of needing to integrate a multitude of components to get to an application, they can now focus on study specific content, such as sensing, user adaption, dialogues or agent behaviours.

The viability of the platform has already been shown by uptake not only in student teaching, but also in at least 4 funded research projects that employ the full setup (one on agents and touch, one on multi-modal output generation, one on robots for autism, and one that involves user speech recognition); and by the interest of other research groups to not only use the platform, but actively contribute to the initiative.

Figure 3 :

 3 Figure 3: A schematic representation of the topic selection process. The TSE uses information stored in the WWS to select a relevant topic and return a topic message.

Figure 5 :

 5 Figure 5: An agent and Wizard of Oz interface deployed across multiple devices.

Block colours indicate components that run on:

		AWARE app	Weight	Blood pressure
	SENSE		UniversAAL (Bluetooth app)
		Holistic Behaviour Analysis Framework (HBAF)
		Short term behaviours	Long term behaviours	Changes in behaviour
	REMEMBER	WOOL Web Service (WWS) Coaching variables and user profile Temporary conversational memory
		Dialog execution engine (Multiple variants:	Topic Selection Engine (TSE)
		WOOL and DAF)
	THINK	Utterance generator		(1) get next topic
		Dialogue execution	Interaction Manager and Intent Planner (Flipper)
					SAIBA / BML interface
	ACT	Wizard controls	User input controls (GUI)		GRETA	ASAP
			COUCH-UNITY UNITY on multiple devices
		Smartphones, wearables, and IoT devices	Servers
		PCs and mobile devices		Experimenter's PC
	Figure 2: The Agents United architecture with its sense, re-
	member, think and act layers. Note that the Holistic Be-
	haviour Analysis Framework (see Section 2.1) and all think
	components can access the WOOL Web Service as it is the
	Shared Knowledge Base (see Section 2.2), but we have min-
	imised those arrows for legibility. We elaborate on the num-
	bered steps (1-4) in Section 2.4 (Flipper).

Agents United Platform: www.agents-united.org

A video showing the use case and interaction can be found at: www.youtube.com/watch?v=lOOEs1OWLkM

ActiveMQ: https://activemq.apache.org/

WOOL Dialogue Platform: www.woolplatform.eu

Yarn Spinner: https://yarnspinner.dev/

Unity3D: www.unity.com

Agents United Platform: www.agents-united.org

Agents United GitHub: www.github.com/AgentsUnited

Platform tutorials: www.github.com/AgentsUnited/documentation/wiki

ACKNOWLEDGMENTS

The authors would like to thank all students and researchers who have worked with various versions of the platform and who have provided valuable feedback for its development.

This work was supported by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No.

(Council of Coaches).

The Agents United Alliance is supported by the Personalised eHealth Technology programme of the University of Twente.