# Evolution of multicellularity by collective integration of spatial information

1. Mathematical Institute, Leiden University; Origins Center, Netherlands
2. Informatics Institute, University of Amsterdam; Origins Center, Netherlands
3. Mathematical Institute, Leiden University; Institute of Biology, Leiden University; Origins Center, Netherlands
Research Article

## Abstract

At the origin of multicellularity, cells may have evolved aggregation in response to predation, for functional specialisation or to allow large-scale integration of environmental cues. These group-level properties emerged from the interactions between cells in a group, and determined the selection pressures experienced by these cells. We investigate the evolution of multicellularity with an evolutionary model where cells search for resources by chemotaxis in a shallow, noisy gradient. Cells can evolve their adhesion to others in a periodically changing environment, where a cell’s fitness solely depends on its distance from the gradient source. We show that multicellular aggregates evolve because they perform chemotaxis more efficiently than single cells. Only when the environment changes too frequently, a unicellular state evolves which relies on cell dispersal. Both strategies prevent the invasion of the other through interference competition, creating evolutionary bi-stability. Therefore, collective behaviour can be an emergent selective driver for undifferentiated multicellularity.

## eLife digest

All multicellular organisms, from fungi to humans, started out life as single cell organisms. These cells were able to survive on their own for billions of years before aggregating together to form multicellular groups. Although there are trade-offs for being in a group, such as sharing resources, there are also benefits: in a group, single cells can divide tasks amongst themselves to become more efficient, and can develop sophisticated mechanisms to protect each other from harm. But what compelled single cells to make the first move and aggregate into a group?

One way to answer this question is to study the behaviour of slime moulds. These organisms exist as single cells but form colonies when their resources run low. Researchers have observed that slime mould colonies can navigate their environment much better than single cells alone. This property suggests that the benefits of moving together as a collective could be the driving factor propelling single cells to form groups.

To test this theory, Colizzi et al. developed a computer model to examine how well groups of cells and lone individuals responded to nearby chemical cues. Unlike previous simulations, the model created by Colizzi et al. did not specify that being in a group was necessarily more favourable than existing as a single cell. Instead, it was left for evolution to decide which was the best option in response to the changing environmental conditions of the simulation.

The mathematical model showed that groups of cells were generally better at sensing and moving towards a resource than single cells acting alone. Single cells moved at the same speed as groups, but they often sensed their environment poorly and got disorientated. Only when the environment changed frequently, did cells revert back to the single life. This was because it was no longer beneficial to band together as a group, as the cells were unable to sense the environmental cues fast enough to communicate to each other and coordinate a response.

This work provides insights into what drove the early evolution of complex life and explains why, under certain conditions, single cells evolved to form colonies. Additionally, if this model were to be adopted by cancer biologists, it could help researchers better understand how cancer cells form groups to move and spread around the body.

## Introduction

The evolution of multicellularity is a major transition in individuality, from autonomously replicating cells to groups of interdependent cells forming a higher-level of organisation (Buss, 2014; Smith and Szathmary, 1995). It has evolved independently several times across the tree of life (Grosberg and Strathmann, 2007; Parfrey and Lahr, 2013). Comparative genomics suggests (Knoll, 2011), and experimental evolution confirms (Boraas et al., 1998; Ratcliff et al., 2012) that the increase of cell–cell adhesion drives the early evolution of (undifferentiated) multicellularity. Increased cell adhesion may be temporally limited and/or may be triggered by environmental changes (e.g. in Dictyostelids and Myxobacteria [Du et al., 2015; Kaiser et al., 1979]). Moreover, multicellular organisation may come about either by aggregation of genetically distinct cells or by incomplete separation after cell division (King, 2004; Du et al., 2015).

The genetic toolkit and the cellular components that allow for multicellularity - including adhesion proteins - pre-date multicellular species and are found in their unicellular relatives (Rokas, 2008; Prochnik et al., 2010; Du et al., 2015; Richter et al., 2018). Aggregates of cells can organise themselves by exploiting these old components in the new multicellular context, allowing them to perform novel functions (or to perform old functions in novel ways) that may confer some competitive advantage over single cells. Greater complexity can later evolve by coordinating the division of tasks between different cell lineages of the same organism (e.g. in the soma-germline division of labour), giving rise to embryonic development. Nevertheless, the properties of early multicellular organisms are defined by self-organised aggregate cell dynamics, and the space of possible multicellular outcomes and emergent functions resulting from such self-organisation seems large – even with limited differential adhesion and signalling between cells. However, the evolution of emergent functions as a consequence of adhesion-mediated self-organisation has received little attention to date.

Mathematical models can define under which conditions multicellularity evolves, in terms of fitness for individual cells vs. the group, or in terms of the resulting spatial and temporal organisation. The formation of early multicellular groups has been studied in the context of the evolution of cooperation: by incorporating game theoretical interactions and transient compartimentalisation (Garcia et al., 2014) or the possibility of differential assortment (Joshi et al., 2017), it was found that adhering groups of cooperating individuals evolve. Alternatively, reproductive trade-offs can give rise to division of labour (Solari et al., 2013) and lead to the formation of a higher-level proto-organism capable of self-regeneration in a structured environment (Duran-Nebreda et al., 2016). A plethora of multicellular life-cycles can emerge by simple considerations about the ecology of the uni-cellular ancestor and the fitness benefit that cells acquire by being in groups (Staps et al., 2019). Once multicellular clusters are established, the spatial organisation of their composing cells can play an important role in determining group-level reproduction - possibly leading to the evolution of cell-death (Libby et al., 2014) or different cell shapes (Jacobeen et al., 2018), and to specific modes of fragmentation of the aggregate (Pichugin et al., 2017; Gao et al., 2019) that increase overall population growth.

In these models, multicellularity is either presupposed or its selective pressure is predetermined by social dynamics, by directly increasing fitness of cells in aggregates or by adverse environmental conditions that enforce strong trade-offs. Here we investigate the origin of this selective pressure, motivated by the idea that multicellular groups emerge as a byproduct of cell self-organisation and cell-environment interactions, and subsequently alter the evolution of their composing cells. We expect that a selective pressure to aggregate can arise from the emergent functions of the multicellular group, without requiring explicit selective advantages and disadvantages for cells in a group. We therefore present a computational model of an evolving population of cells where fitness is based solely on how adequately a cell responds to a spatially and temporally heterogeneous environment, regardless of whether they belong to an aggregate.

In this study, we draw inspiration from collective movement of groups of cells, such as the aggregate phase of the slime mould Dictyostelium discoideum (Schaap, 2011), other simple multicellular organisms (Kaiser, 2003; Schaap, 2011; Smith et al., 2019) and many processes within complex multicellular organisms, for?example, embryogenesis, tissue repair and cancer (Weijer, 2009; Friedl and Gilmour, 2009). Previous models have shown how cell collectives are able to integrate noisy information from the environment, for instance when moving up a shallow chemoattractant gradient. (Marée et al., 1999; Szabó et al., 2006; Kabla, 2012; Szabó et al., 2010; Camley and Rappel, 2017; George et al., 2017; Camley, 2018; Varennes et al., 2017).

We use the Cellular Potts Model (Graner and Glazier, 1992) (CPM) to study collective cell movement as an emergent driver of multicellularity during evolution. The CPM formalism is a spatially extended, mesoscopic description of cells which explicitly accounts for cell shape and size, and allows for a straightforward implementation various cellular processes within complex and potentially self-organised environments. We include four key elements: cells are placed in a seasonally changing environment that periodically introduces new resources at different locations, they can perform chemotaxis by sensing a chemoattractant produced by these resources, they reproduce depending on their proximity to resources and they can evolve their adhesion to other cells. Because the gradient generated by the resources is noisy and shallow, we find that individual cells follow the chemotactic signal very inefficiently. Instead, cells that adhere to each other within groups transfer information about the gradient in a self-organised manner, allowing for efficient chemotaxis in our model. We show that for longer seasons, this emergent property of cell groups is sufficient to select for high levels of adhesion and multicellularity, despite the fact that fitness is only defined at the cell level.

## Results

### Model setup

#### Cell model

We consider a population of N cells that search for resources on a surface to be able to replicate. Cells are modelled with a 2D hybrid Cellular Potts Model (CPM) (Graner and Glazier, 1992; Glazier and Graner, 1993; Daub and Merks, 2015) on a square lattice of size $L2=500×500$ sites. The CPM formalism captures the fact that biological cells are dissipative objects with deformable boundaries. A cell consists of multiple adjacent lattice sites. The sites not occupied by cells are the medium, which contributes to determining the adhesive properties of a cell, but has no further properties. All the lattice sites belonging to one cell have the same identification number, different from that of any other cell or medium. Cell movement arises from stochastic fluctuations (extensions and retractions) of the cell boundaries. These fluctuations are generated by forces arising from cell size maintenance, adhesion and migration (explained below). We calculate these forces by minimising the corresponding energy function with the Metropolis algorithm (with a temperature-like parameter T that scales the overall probability of membrane fluctuations). Lattice sites are updated in random order. In one Monte Carlo Step (MCS), $L2$ lattice sites are updated.

To model cells as elastic and deformable objects, we assume that cell size - the number of lattice sites it is made up of - remains close to a preferred value $AT$ equal for all cells (set to 50 lattice sites unless explicitly stated), and deviations are resisted with a stiffness parameter $λ$. Cells adhere to each other if they express matching ligands and receptors on their surface. Ligands and receptors are modelled as bit strings of length $ν$ (Figure 1a), and are assumed to be expressed constitutively and uniformly on the membrane. Adhesion strength increases linearly with the number of complementary bits in the ligand and receptor. In the CPM, adhesion strength is expressed in terms of the interfacial energy $Jc,c$. For each pair of adjacent lattice sites belonging to different cells, the interfacial energy $Jc,c$ is calculated from the cells’ ligands and receptors. A larger complementarity corresponds to lower values of $Jc,c$ (i.e. lower energy level in the bound state) and thus stronger binding. For cells adjacent to the medium, an additional cell-medium contact energy $Jc,m$ is calculated based on the similarity between part of their ligand bit string and an arbitrary target string. Cells adhere when cell–cell contact energy and medium-medium energy (equal to zero by definition) are lower than cell-medium contact energy: $(Jm,m+Jc,c)/2. Cell adhesion can be characterised through the surface tension $γ=Jc,m-Jc,c/2$ (Glazier and Graner, 1993). Cells adhere when $γ>0$ and disperse for $γ<0$. Note that the value of $γ$ is due to a balance between $Jc,c$ and $Jc,m$, such that cells achieve higher surface tension either by increasing the number of complementary ligand-receptor pairs or by reducing the similarity of their ligands to the medium target string. Modelling ligands and receptors separately allows for sufficient variability of differential adhesion, without predetermining the J values between cells. For example, it allows for any combination of adhesion strengths between three (or more) cells.

Figure 1

During chemotaxis, eukaryotic cells repeatedly reorient the polymerisation of the actin cytoskeleton. This reorientation takes some time (Ridley et al., 2003), resulting in a migration pattern that is persistent over short time scales. We emulate this by combining a model of persistent migration (Beltman et al., 2007) with chemotaxis. Following (Beltman et al., 2007), persistent migration occurs through biasing cell membrane fluctuations towards the previous direction of motion of a cell (Figure 1b). The strength of the bias is quantified by $μp$, and the direction of motion is updated every $τp$ MCS with the direction of actual cell displacement. This model of cell migration generates a persistent random walk (Beltman et al., 2007). Chemotaxis biases cell motion (with strength $μχ$) towards higher local concentrations of a chemoattractant (Figure 1c,d). We assume that this chemoattractant is released at low concentration by resources present at one end of the grid, creating a shallow and noisy gradient over the grid (Figure 1c). For simplicity, we model a shallow, linear and noisy gradient decreasing from the source with slope $kχ$ (in unit of percent decrease over unit distance), and a heterogeneous substrate on which the chemoattractant may not attach (with probability $pχ=0$). Because of the noise in the gradient, the direction of cells’ chemotaxis may be different from the correct direction of the gradient. We used this model setup to assess the properties of single-cell vs. collective migration.

### Evolutionary model

To explore the evolutionary dynamics of a population of cells, we seasonally change the location of the resources, and therewith the direction of the gradient, every $τs$ MCS (Figure 2). Longer seasons (larger values of $τs$) correspond to more persistent resources. During each season (i.e. one period of $τs$ MCS) cells move due to chemotaxis and persistent migration. Depending on the ligands and receptors expressed on the cell surfaces, they may either adhere to one another or disperse from one another (Figure 2a). At the end of the season, cells are given a chance to divide, followed by a culling phase to keep the number of cells constant. To reflect the assumption that more nutrients are present at higher concentrations of the signal, the division probability is inversely proportional to the distance of the cell to the gradient peak and cells very close to the gradient peak may divide multiple times. Cells divide along their short axis to create two daughter cells (after Hogeweg, 2000), after which we let cells regrow to target size for 5 MCS. The daughter cells inherit mutated copies of the ligand and receptor, so that their adhesive properties can change with respect to the parent. This allows cells to evolve their adhesion strength. Cell size $AT$, strength of chemotaxis $μχ$ and migration persistence $μp$ do not evolve. After cell division, the population is brought back to N cells by randomly culling cells, at which point the new season begins (Figure 2b). Note that we do not include cell dispersal after replication, therefore related cells remain close at the beginning of the new season. Simulations last 400 seasons (i.e. $400×τs$ MCS), which is sufficient to reach evolutionary steady state under all conditions.

Figure 2

We do not select for multicellularity directly: the fitness function rewards cells for their proximity to resources, and we do not explicitly incorporate a fitness advantage or disadvantage for the multicellular state. Therefore, multicellular clusters (Figure 2a,c,d) can arise only because they perform an emergent task that single cells cannot perform.

See Table 1 for parameter values, and Materials and methods Section for the details of the model and parametrisation.

Table 1

### Strongly adhering cells perform efficient collective chemotaxis

We first assessed how well groups of cells with different adhesion strengths could reach the source of the chemotactic signal. We placed a connected cluster of cells on one side of the lattice, opposite to the location of the gradient peak. We then recorded their travel distance over a fixed amount of time and compare it to the travel distance of single cells (i.e. from simulations with only one cell), by measuring both the position of the centre of mass of the group (Figure 3a) and the position of the cell closest to the peak of the gradient (Figure 3b). Single cells perform chemotaxis inefficiently (Video 1), whereas a group of adhering cells migrates up the same gradient more accurately (Figure 3a, $γ>0$): the centre of mass of this group takes much less time than single cells do to reach the peak of the gradient (Video 2). Groups of cells can also perform collective chemotaxis when they do not adhere, and when they do not have a preference for medium or cells, although with lower efficiency in both cases (Figure 3a, respectively $γ<0$ and $γ=0$). Chemotaxis is inefficient, because these cells tend to lose contact from one another (Video 3) and once isolated they behave like those from simulations with one cell (Figure 3a,b ‘one cell’). Single cells also show large variance between different simulations (Figure 3b). While cell clusters perform chemotaxis efficiently only when cells adhere, the speed of the cell closest to the peak of the gradient is roughly the same regardless of adhesion strength (Figure 3b). Thus, in a non-adhering population some cells reach the peak of the gradient almost as quickly as an adhering cluster does.

Figure 3
Video 1
Video 2
Video 3

Adhering cells have large chemotactic persistence - as shown by the super-linear shape of the Mean Square Displacement (MSD) plot (Figure 3c, $γ=6$) and by a diffusive exponent consistently larger than 1 (Figure 3d; the diffusive exponent is obtained as the derivative of the log-log transformed MSD/time curve, see Appendix 1.1). Instead, the MSD of a single cell (Figure 3c, one cell) is approximately linear and its diffusive exponent tends to 1, indicating that cells’ movement is much more dominated by diffusion. Interestingly, there is no difference in the instantaneous speed of cells when they are in a cluster or when they are alone (Figure 3e), so the higher rate of displacement of a group of adhering cells is only due to larger persistence in the direction of motion. Figure 4 shows the movement of a cluster of strongly adhering cells ($γ=6$) compared to the movement of a single cell, over the typical setup of the simulation system. Although the cluster moves straight towards the source of the gradient, individual cells follow noisy trajectories.

Figure 4

A possible explanation for collective chemotaxis is that a cluster averages individual cells’ polarisation, leading to a linear relationship between the accuracy of chemotaxis and the number of cells in the cluster (Varennes et al., 2017). Instead, we found that cluster speed saturates quickly with the number of cells, at a smaller speed than that of individual cells (cf. Appendix 1.2 with Figure 3e). We conclude that individual contributions to cluster chemotaxis are not simply averaged. Therefore, we look at how cells self-organise to understand how collective chemotaxis comes about.

Through persistent migration, a cell pushes other cells within an adhering cluster, and is pushed by them. The resulting forces are resolved when cells align and form streams within the cluster (see Video 4). These streams are persistent over a much longer time scale than a cell’s persistence $τp=50$ MCS (since the video frame rate is 50 MCS and streams are visible over multiple frames). Through streaming, these small clusters generate extensions, retractions and rotations (Video 4), so that the entire cluster visually resembles a single amoeboid cell (Video 2). This behaviour is not influenced by the presence of the chemotactic signal, since the flow field is identical when the chemotactic signal is removed (Appendix 1.3). Thus, the effect of persistent migration is to align the direction of motion of the cells in a cluster. This in turn speeds up collective chemotaxis, as cell streams preferentially align towards the direction of the gradient, although aligning is not strictly required for chemotaxis (Appendix 1.4). Clusters perform chemotaxis faster than individual cells over a large range of values for persistent migration strength $μp$ and chemotactic strength $μχ$ (Appendix 1.5 and Appendix 1.6), with larger $μp$ increasing collective chemotaxis speed (and to a lesser extent individual chemotaxis speed) more than $μχ$. Because larger cells perceive a larger area of the chemotactic signal, chemotactic migration improves with cell size (Appendix 1.7).

Video 4

We calculated the deviation of each individual cell’s measurement of the gradient as the angle $θ?(X→,χ→)$ between the true direction of the gradient $X→$ and the direction of the gradient locally measured by the cells $χ→$ (so that $θ?(X→,χ→)=0$ is a perfect measure). We found that the measurements of individual cells deviate significantly from the true direction of the gradient (Figure 3f). Despite this, they are carried in the right direction by the other cells. To assess how cells in a cluster alter each others’ (short-timescale) trajectories we extracted the straight segments from the cell tracks and assessed both the length of these segments and their orientation with respect to the gradient source (Appendix 1.8). We find that cells in a cluster tend to migrate for longer in straight lines, and that these straight lines are also more likely to be oriented towards the source of the gradient (Figure 3g). For single cells, there is no such bias.

In conclusion, cluster organisation emerges from cells altering each others’ paths by exerting pushing and pulling forces through their persistent migration, which in turn results in efficient collective chemotaxis.

### The evolution of uni- or multicellular strategies depends on season duration

The emergence of reliable chemotactic behaviour in adhering cell clusters suggests an evolutionary path to multicellularity: a population of cells may aggregate if collective chemotaxis allows cells to find resources more reliably. While cells could improve their ability to sense the gradient individually by becoming bigger, there are many factors that restrict cell size, such as the complexity of the metabolism and cellular mechanisms such as cell division (Bj?rklund and Marguerat, 2017; Marshall et al., 2012). We therefore assume that cell size is fixed, and we let cells evolve adhesion - that?is, the receptor and ligands expressed by the cells - in response to a seasonally changing environment, where the gradient is generated by a volatile resource that periodically changes position. Cells closer to the peak of the gradient have a higher chance to reproduce at the end of the season, and related cells remain close to each other at the beginning of the new season (there is no cell dispersal phase, see also model setup and Materials and methods). The receptors and ligands of the initial population are chosen such that cells neither adhere to one another nor disperse from one another ($γ=0$).

When the season lasts $τ=100×103$ MCS, the average adhesion between cells readily increases after only few generations (Figure 5a): $Jcell,cell$ decreases and $Jcell,medium$ increases (see also Video 5 and Figure 2 for snapshots). At evolutionary steady state, all cells adhere strongly and with roughly the same energy to one another (Appendix 2.1). Figure 5b shows that two evolutionary steady states are possible, depending on the duration of the season $τs$. For $τs<20×103$ MCS, cells evolve to become unicellular, as cell–cell interactions are characterised by strong repulsion ($γ<0$). Figure 5c suggests that by selecting for $γ<0$ cells disperse efficiently throughout the grid. Although non-adhering cells follow the chemotactic signal only weakly, the spreading over the course of multiple seasons ensures that at least some cells end up close to the source of the gradient at the end of the season (Video 6). In contrast, a cluster of adhering cells is at disadvantage when seasons are short because it does not have enough time to reach the source of the chemotactic signal. Over the course of multiple seasons, an adhering cluster ends up in the centre of the lattice (Video 7) and all its composing cells have the same (low) fitness. Furthermore, the connectedness of a cluster of adhering cells is locally disrupted when excess cells are culled between seasons (Figure 2b), which briefly reduces the efficiency of collective migration. Because this phase is short-lived - cells reconnect within 2000 MCS - we expect that culling plays a minor role in the evolutionary outcome of the system. For $τs>40×103$ MCS, cells evolve to adhere to one another, i.e. $γ>0$ (see Figure 5c for a snapshot). When seasons are sufficiently long, clusters of adhering cells have enough time to reach the source of the gradient. At this point, the fitness of cells within a cluster outweighs that of non-adhering cells, because clustering increases the chances of reaching the peak of the gradient. Finally, for intermediate season duration, $20×103≤τs≤40×103$ MCS, both repulsion and adhesion are evolutionary (meta) stable strategies, and the outcome of the simulation depends on the initial value of $γ$ (for $τs=20×103$ MCS, the steady state with $γ>0$ is very weakly stable).

Figure 5
Video 5
Video 6
Video 7

Because different values for migration parameters affect collective chemotaxis speed, we checked that the evolution of multicellularity is qualitatively robust to changes in the values of persistent migration strength $μp$ and chemotactic strength $μχ$ (respectively in Appendix 2.2 and Appendix 2.3). Results are also robust to changes in gradient shape (assuming that resources are located over an entire side of the lattice, we tested a gradient with straight isoclines in Appendix 2.4) and to steeper, noiseless gradients (Appendix 2.5). Furthermore, the evolution of multicellularity does not depend on the precise mechanism for collective chemotaxis. To show this, we relax the assumption that individual cells sense the gradient by implementing a recently proposed mechanism of emergent collective chemotaxis that relies only on concentration sensing (Camley et al., 2016). Following Camley et al., 2016, we assume that cell polarisation is inhibited at the sites of cell–cell contact (a phenomenon called contact inhibition of locomotion, see Mayor and Carmona-Fontaine, 2010 for a review), and that the magnitude of their polarisation is proportional to the concentration - not the gradient - of the chemoattractant. In Appendix 3.1, we show that results are robust to this modification of the chemotaxis mechanism.

### Interference competition between unicellular and multicellular strategies causes evolutionary bi-stability

We next investigated what causes the evolutionary bi-stability in adhesion strategies for season duration $20×103≤τs≤40×103$ MCS. We performed competition experiments between two populations of cells, one adhering ($γ=6$) and one non-adhering ($γ=-4$), to determine whether a strategy can invade in a population of cells using the other strategy. We simulated non-adhering mutants invading a resident population of adhering cells by placing a large cluster of adhering cells in front of a small group of non-adhering ones (Figure 6a), and conversely, a small cluster of adhering cells invading a large group of non-adhering cells (Figure 6b). This initial configuration is analogous to the beginning of a season in the evolutionary experiments, as mutants are in small numbers and furthest away from the new peak because they are likely born from cells that replicate most, that?is, those closest to the previous location of the peak. In both cases, after $30×103$ MCS, the resident population physically excludes the invading one from the path to resources, and thus the distance travelled by the invading population is limited. This shows that the adhesion energy of the resident population (whether cells adhere or not) determines the outcome of the invasion (for the values of $τs$ where we find evolutionary bistability). We also considered a scenario where a whole population - rather than few mutants - invades another with the opposite strategy. We studied the spatial competition dynamics of two clusters of equal size ($N=100$ cells) when adhering cells are positioned in front of the non-adhering ones (Figure 6c), and when the position of the two clusters is swapped (Figure 6d). The distance to the peak after 30 × 103 MCS of a cluster of adhering cells is larger (i.e. their fitness is smaller) if they are hindered by a population of non-adhering cells in front of them. Taken together, these results show that there is interference competition (i.e. direct competition due to displacement) between populations of cells with different strategies. In the evolutionary experiments, mutants with a slightly different strategy are generated during reproduction at the end of each season and interference competition continually prevents their successful invasion for intermediate season duration. This explains why the two strategies are meta-stable. This result may also provide a simple explanation for the fact that many unicellular organisms do not evolve multicellularity despite possessing the necessary adhesion proteins. Moreover, evolutionary bi-stability protects the multicellular strategy from evolutionary reversal to unicellularity over a large range of environmental conditions.

Figure 6

### Multicellularity and the cost of adhesion

So far, we showed that the evolutionary benefit of uni- or multi-cellular strategies is indirect, as it is mediated by the fittest form of self-organisation for a given season duration. For simplicity, we did not incorporate any cost to evolving multicellularity. However, evolving multicellular organisms may incur fitness costs that are not present at the unicellular level (Rebolleda-Gómez and Travisano, 2018; Rainey and Rainey, 2003; Ratcliff et al., 2012; Yokota and Sterner, 2011; Kapsetaki and West, 2019). We incorporated costs in our system by assuming that cells spend energy to maintain their bonds with other cells, with a cost cm (per unit of cell boundary, per MCS). This metabolic cost accumulates over time when cells are in contact with one another, and translates into a fitness penalty at the end of the season for cells that spent more time in contact with others. Costs range from $cm=0$, the cost-free model presented so far, to $cm=1$ (the maximum cost) which zeroes the fitness of a cell that spent the entire season completely surrounded by other cells. Multicellularity evolved for sufficiently long seasons when costs were not too high ($cm≤0.5$), with larger costs shifting the transition to multicellularity to longer seasons, while only the uni-cellular strategy evolved when costs were high ($cm=0.75$, for the season duration we tested; Appendix 4.1).

## Discussion

We demonstrated that undifferentiated multicellularity can evolve in a cell-based model as a byproduct of an emergent collective integration of spatial cues. Previous computational models have shown that multicellularity can be selected by reducing the death rate of cells in a cluster (Staps et al., 2019; Pichugin et al., 2017), through social interaction (Garcia and De Monte, 2013; Joshi et al., 2017), by incorporating trade-offs between fitness and functional specialisation (Ispolatov et al., 2012), or by allowing cells to exclude non-cooperating cells (Pfeiffer and Bonhoeffer, 2003). In these studies, direct selection for forming groups is incorporated by conferring higher fitness to the members of a cluster.

Earlier work found that multicellular structuring can emerge without direct selection when cells are destabilised by their internal molecular dynamics (e.g. the cell cycle) (Furusawa and Kaneko, 2002), or because of a toxic external environment (Duran-Nebreda et al., 2016). In both cases, cell differentiation stabilises cell growth and arises as a consequence of physiological or metabolic trade-offs. With our model setup, we show that division of labour - although important - is not a strict requirement for emergent aggregation. Nevertheless, our work bears some similarity with these models because we do not explicitly incorporate a fitness benefit for being in a group: selection acts on individual cells only on the basis of how close they are to the source of the gradient, regardless of migration strategy. Thus the fitness function does not dictate which evolutionary strategy, that?is, uni- or multi-cellularity, should be followed.

A limitation of the current model is that cells have a narrow set of possibilities for adapting to the environment, as the only mutable traits are their ligands and receptors. Therefore, their adaptation to the environment is solely mediated by their adhesion to one another and selection for multicellularity can only occur because adhering clusters always perform chemotaxis better than individual cells. Despite the advantage of clusters over individuals, an alternative strategy can evolve that does not rely on collective behaviour. This uni-cellular strategy evolves because non-adhering cells disperse throughout the field over multiple seasons. By chance - and aided by inefficient chemotaxis - some cells will be located near the peak of the gradient at the end of each season. When seasons change rapidly, a multicellular cluster does not have the time to reach the peak of the gradient. It is therefore at disadvantage over cells evolving a unicellular strategy. This further illustrates that the selection pressure to become multicellular emerges from the structure of the environment in our model, rather than being an explicit part of the fitness function. Whichever evolutionary strategy maximises fitness, be it multi- or uni- cellularity, will evolve within the (limited) complexity of the model.

A second limitation of the model is that resources are modelled only implicitly - through the chemoattractant gradient they generate and through season duration, i.e. how long they persist. The precise seasonality of these resources might be realistic if resources are deposited in the system by periodic phenomena (e.g. tides, or daily and yearly cycles), whereas other types of resources might be more stochastic (such as preys). However, if the stochasticity of resources is not too extreme, we expect that evolution converges to the average resource duration.

In many ways, the evolution of multicellularity can be compared to the evolution of collective dynamics. Previous studies on the evolution of herding behaviour showed that aggregating strategies can also evolve in response to highly clumped food even though the pack explores the space slowly and inefficiently before finding food (Wood and Ackland, 2007). When gradient sensing and social behaviour are both costly, a combination of strategies evolves in response to selection for distance travelled (Guttal and Couzin, 2010). Some individuals pay the cost for actively sensing the gradient, while others invest in social behaviour to move towards others and align their direction of motion with them, leading to the formation of migrating herds (Guttal and Couzin, 2010). These models of collective migration represent individuals as active particles, which is similar to the behaviour of our cells. However, group movement requires an explicit rule for alignment, whereas in our model it emerges naturally from interactions between deformable cells. Modelling cells with an explicit shape and size (including both CPM and, we expect, self-propelled particles) allows for spatial self-organisation and can generate interesting ecological dynamics, such as interference competition between the unicellular and multicellular search strategies. The ensuing evolutionary bi-stability stabilises unicellularity despite these cells possessing the surface protein toolkit to adhere to each other, and prevents multicellular organisation from evolutionary reversal into single cells (over a range of environmental conditions). The ‘automatic’ outcome of spatial self-organisation provides an initial, non-genetic robustness, which can be further stabilised by later adaptations (Libby et al., 2016).

In our model, cells retain their spatial distribution between seasons. This reinforces spatial self-organisation, and consequently bistability, because genetically similar cells remain close to one another. However, we expect bistability also if cells were dispersed between seasons: few adhering cells scattered in a cloud of non-adhering ones would not be likely to meet (and collectively chemotax) if seasons are short. In contrast, a large number of adhering cells would meet frequently after scattering and thus displace non-adhering cells in their march towards the peak of the gradient. This suggests that the two strategies are not mutually invadable over some intermediate season length, hence bistability.

The driver for the evolution of adhesion in our model is collective chemotaxis. This is reminiscent of the aggregate phase of the life cycle of Dictyostelium discoideum (Schaap, 2011), in that a cluster of cells moves directionally as a unit following light or temperature, while individual cells inefficiently identify the correct direction of motion (Miura and Siegert, 2000). There are some important differences between our model and D. discoideum, however. Information about the direction of the gradient is transmitted mechanically within cell clusters in our model. In D. discoideum photo- and thermo-taxis are coordinated by waves of cAMP secretion that travel through the slug. The lack of extra chemical cues to organise movement within a cell cluster in our model makes for a simpler scenario without large-scale transmission of information throughout the aggregate. Nevertheless, computational modelling has shown that long-range chemical signalling coupled to cells’ differential adhesion suffices to reproduce D. discoideum’s migration (Marée et al., 1999; Marée and Hogeweg, 2001). Another important difference between our model and D. discoideum is the absence of dispersal at the end of the life cycle in our model. In D. discoideum, the slug transforms into a fruiting body at the top of a stalk of terminally differentiated cells. Extending the current model with the evolution of dispersal would enrich our understanding of D. discoideum evolution towards partial multicellularity.

Our model of collective movement is an example of the ‘many wrongs’ principle (Simons, 2004): the direction error of each cell is corrected by the interactions with the other cells in the cluster. However, in our model there is no explicit mechanism for transferring gradient information between cells. Therefore our results differ from previous work on rigid clusters of cells, where cells’ polarisation towards the perceived gradient translates linearly and instantaneously to cluster movements (Camley et al., 2016; Varennes et al., 2017). In models where cells readily exchange neighbours, simple rules for cell adhesion and migration led to self-organisation of cells into highly persistent, migrating tissue with emergent global polarity (Smeets et al., 2016) (earlier observed also at large cell density without adhesion rules [Beltman et al., 2007; Szabó et al., 2006]). Similarly, in our model, cells convey gradient information through such emergent collective streaming, which becomes biased towards the (weak) chemotactic signal. However, we expect that the evolutionary results described here are independent of the particular cell model choice, or the mechanism for chemotaxis provided that cells were able to polarise or move also in the absence of other cells. Indeed, we found similar results with an alternative model of collective chemotaxis (Camley et al., 2016) in which individual cells do not sense the gradient.

We opted for a computational cell-based model - the Cellular Potts Model - because it allowed us to explore the spatial interactions of cells, and because it enabled straightforward implementation of the evolvable receptor-ligand system. The visual nature of our results may guide the future development of analytical approaches to generalize the results of this work. For instance, analytical work may provide a more detailed explanation of the ‘many wrongs’ principle for a cell cluster in which cells are highly motile and change their neighbours often, in which case positional information is transmitted by pulling and pushing on each other. Moreover, the simplicity of our model setup makes our results easily testable in vitro.

The importance of a bottom-up approach to study the evolution of multicellularity has been repeatedly emphasised (van Gestel and Tarnita, 2017; De Monte and Rainey, 2014), and a broader understanding of cells self-organisation and evolution may have applications to clinically relevant multiscale evolutionary problems, such as the evolution of collective metastatic migration of cancer cells (Coffey, 1998; Stuelten et al., 2018; Disanza et al., 2019; Lacina et al., 2019). Our work highlights that the properties of single cells emergently give rise to novel properties of cell clusters. These novel properties - in a downward causative direction - generate the selection pressure to form the first undifferentiated multicellular groups.

## Materials and methods

We model an evolving population of cells that migrate and perform chemotaxis on a 2-dimensional lattice. Cell–cell interactions and movements are modelled with the Cellular Potts Model (CPM) (Graner and Glazier, 1992; Glazier and Graner, 1993) and simulated with a Monte Carlo method. The evolutionary dynamics (mutations and selection) are implemented assuming constant population size ($N=200$ cells). Cells undergo fitness-dependent reproduction after every season which lasts $τs$ Monte Carlo Steps of the CPM algorithm, and then the population is culled back to its original size. After this, environmental conditions are changed and a new season begins. Parameter values are motivated throughout this section, and summarised in Table 1. The custom software used for the simulations and to generate the figures is available at Colizzi and Vroomans, 2020.

### Cell dynamics

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The model is a hybrid Cellular Potts Model implemented with the Tissue Simulation Toolkit (Daub and Merks, 2015). A population of N cells exists on a regular square lattice $Λ1??2$. The chemotactic signal is located on a second plane $Λ2$, of the same size and spacing as $Λ1$. A cell c consists of the set of (usually connected) lattice sites $x→∈Λ1$ to which the same spin s is assigned, that?is, $c?(s)={x→∈Λ1∣σ?(x→)=s}$. The spin value is a non-negative integer, it is unique and positive for each cell, and it is used as the cell identifier. The medium is assigned spin $σ=0$.

Cell movement arises from deformation of its boundaries through stochastic fluctuations. These fluctuations minimise a cell’s energy, whose terms correspond to biophysically motivated cell properties (but see Glazier, 2007?for a discussion on the statistical mechanics of the CPM). The energy minimisation occurs through the Metropolis algorithm (a Monte Carlo method), as follows. Fluctuations in cell boundary attempt to copy the spin value $σ?(x→)$ of a randomly chosen lattice site $x→$ to a site $x→?′$ in its Moore neighbourhood. One Monte Carlo Step (MCS) consists of $L2$ attempted copying events, with $L2=|Λ1|$ (the size of the lattice, and L one of its dimensions on a regular square lattice). Throughout this work $L=500$. Whether an attempted spin copy is accepted depends on the contribution of several terms to the energy H of the system, as well as other biases Y. A copy is always accepted if energy is dissipated, that?is, if $Δ?H+Y<0$ (with $ΔH=Hafter?copy?Hbefore?copy$), and may be accepted if $Δ?H+Y≥0$ because of ‘thermal’ fluctuations following a Boltzmann distribution:

$P?\left(\mathrm{\Delta }?H,Y\right)={e}^{\frac{-\left(\mathrm{\Delta }?H+Y\right)}{T}}$

with $T=16$ the Boltzmann temperature, a temperature-like parameter (in Arbitrary Units of Energy AUE) that controls the overall probability of energetically unfavourable fluctuations (allowing escape from local energy minima). The Hamiltonian H of the system consists of two terms, corresponding to adhesion and cell size maintenance:

$H={H}_{\mathrm{a}\mathrm{d}\mathrm{h}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}+{H}_{\mathrm{c}\mathrm{e}\mathrm{l}\mathrm{l}\text{?}\mathrm{s}\mathrm{i}\mathrm{z}\mathrm{e}}$

The copy biases, or ‘work terms’, Y consist of terms corresponding to cell migration and chemotaxis:

$Y={Y}_{\mathrm{m}\mathrm{i}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}+{Y}_{\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{x}\mathrm{i}\mathrm{s}}$

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Adhesion between cells and to medium contribute to the Hamiltonian as:

${H}_{\mathrm{a}\mathrm{d}\mathrm{h}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}=\sum _{\left(\stackrel{\to }{x},{\stackrel{\to }{x}}^{\text{?}\mathrm{\prime }}\right)}J\left(\sigma \left(\stackrel{\to }{x}\right),\sigma \left({\stackrel{\to }{x}}^{\text{?}\mathrm{\prime }}\right)\right)\left(1?\delta \left(\sigma \left(\stackrel{\to }{x}\right),\sigma \left({\stackrel{\to }{x}}^{\text{?}\mathrm{\prime }}\right)\right)\right)$

where the sum is carried out over all the neighbour pairs $(x→,x→?′)$, and $δ?(σ?(x→),σ?(x→?′))$ is the Kronecker delta which restricts the energy calculations to neighbouring lattice sites at the interface between two cells, or a cell and medium. $J?(σ?(x→),σ?(x→?′))$ is the contact energy between two adjacent lattice sites $x→$ and $x→?′$ with different identity (i.e. $J=0$ when $σ(x→)=σ(x→?′)$).

In order to calculate the values of $J?(σ?(x→),σ?(x→?′))$, we assume that cells express ligand and receptor proteins on their surface. Ligands and receptors are modelled as binary strings of fixed length $ν$ (Figure 1, inspired by Hogeweg, 2000). Two cells adhere more strongly (experience lower J values) when their receptors R and ligands I are more complementary, i.e. when the Hamming distance $D?(R,I)=∑i=1νδ?(Ri,Ii)$ between them is larger. Thus, given two cells with spin values $σ1$ and $σ2$ and their corresponding pairs of receptors and ligands $(R?(σ1),I?(σ1))$ and $(R?(σ2),I?(σ2))$:

$J?\left({\sigma }_{1},{\sigma }_{2}\right)={J}_{\alpha }+2?\nu -D?\left(R?\left({\sigma }_{1}\right),I?\left({\sigma }_{2}\right)\right)-D?\left(R?\left({\sigma }_{2}\right),I?\left({\sigma }_{1}\right)\right)$

with $Jα=4$ chosen so that the final calculation yields values for $J?(σ1,σ2)$ in the interval $[4,52]$. For any particular receptor R there is a single ligand I which is maximally complementary, leading to a J value of 4; and a single I which is maximally similar, leading to a J value of 52.

Adhesion of a cell with medium is assumed to depend only on the cell (the medium is inert, that?is, $J(σmedium,σmedium)=0$ ), and in particular it depends only on a subset of the ligand proteins of a cell. This subset consists of the substring of I which begins at the initial position of I and has length $ν′$. The value of $J(σ1,σmedium)$ is calculated as:

$J\left({\sigma }_{1},{\sigma }_{\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{m}}\right)={J}_{\alpha }^{\mathrm{\prime }}+\sum _{i=1}^{{\nu }^{\mathrm{\prime }}}F\left(i\right){I}_{i}$
$F?\left(i\right)=\left\{\begin{array}{cc}4\hfill & \text{if}i=1\hfill \\ 3\hfill & \text{if}i=2\hfill \\ 2\hfill & \text{if}i=3\hfill \\ 1\hfill & \text{if}4\le i\le 6\hfill \\ 0\hfill & \text{if}i>6\text{}\hfill \end{array}$

with $Jα′=8$ and $F?(i)$ a piece-wise defined function (a lookup table). The J values range in the interval (Du et al., 2015; Jacobeen et al., 2018).

Encoding the energy values for cell adhesion in terms of receptor-ligand binding allows for flexibility and redundancy. Two cells that have the same receptors and ligands (i.e. given $R?(σ1),I?(σ1)$ and $R?(σ2),I?(σ2)$ with $R?(σ1)=R?(σ2)$ and $I?(σ1)=I?(σ2)$) can have any J value, by virtue of the particular receptor and ligand combination. The lookup table for the J value with the medium was chosen to allow for a wide variety of possible J values with a small number of bits. Finally, implementing receptors and ligands in terms of binary strings allows for a simple evolutionary scheme, where mutations consist of random bit-flipping (more on this below). The numerical values of the various constants are chosen with four criteria in mind: (1) the receptor-ligand system has to be long enough that many different combinations are possible, so that its evolution is more open-ended; (2) two cells with random receptors and ligands do not - on average - adhere preferentially to each other or to the medium; (3) the range of adhesion energy must allow for strong clustering and strong dispersal while cells maintain their integrity; (4) although we are not fitting cell behaviour to any specific system, the adhesion energies must be in the typical range used to quantitatively model eukaryotic cells with CPM (Graner and Glazier, 1992; Marée and Hogeweg, 2001; Ouchi et al., 2003; Magno et al., 2015). With these constraints we set receptor and ligand lengths to $ν=24$. On average, two cells with random receptors and ligands will neither preferentially adhere to each other nor to the medium if their surface tension $γ=J(σcell,σmedium)?J(σcell,σcell)/2$ (see main text) is zero. We numerically checked (by generating a large number of ligands and receptors) that $?γ(cells?with?random?ligand?receptors)?=?J(σcell,σmedium)?J(σcell,σcell)/2?=?[8,20]???[4,52]?/2=0$. Moreover $γmax=18$ and $γmin=?18$ (parameter values in Graner and Glazier, 1992; Marée and Hogeweg, 2001; Ouchi et al., 2003; Magno et al., 2015).

### Cell size maintenance

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Cell size $A?(c)=|c?(s)|$, the number of lattice sites that compose a cell, is assumed to remain close to a target size $AT$ (equal for all cells). This is achieved by adding an energy constraint in the Hamiltonian that penalises cell sizes that are much larger or smaller than $AT$:

${H}_{\text{cell size}}=\sum _{c?\text{}\in \text{}?C}\lambda ?{\left(A?\left(c\right)-{A}_{T}\right)}^{2}$

with C the set of cells c present in the lattice configuration, and $λ$ a scaling factor for cell stiffness. This formulation captures the fact that cells are elastic objects that resist deformation from a preferred size ($AT$). Unless otherwise specified, $AT=50$ lattice sites, chosen small enough to reduce computational load while large enough to avoid lattice anisotropy effects (Magno et al., 2015). The numerical value of $λ$ (set to 5 throughout the paper) is large enough to preserve cell size but not too large to freeze cells in place (see Graner and Glazier, 1992; Ouchi et al., 2003 for details).

### Cell migration

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We model migration (following Beltman et al., 2007) by biasing cell movement to their previous direction of motion $p→?(c)$: extensions of a cell are energetically more favourable when they are closer to the direction of that cell’s $p→$:

${Y}_{\mathrm{m}\mathrm{i}\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}=?{\mu }_{\mathrm{p}}\mathrm{cos}?\left({\theta }_{p}\right)$

Where $μp$ is the maximum energy contribution given by migration, and $θp$ is the angle between $p→$ and the vector that extends from the centre of mass of the cell to the lattice site into which copying is attempted. Every $τp$ MCS the vector $p→$ is updated: its new value is the vector corresponding to the actual direction of displacement of the cell over the past $τp$ MCS (scaled to unit) (Figure 1). Persistent migration occurs if $τp?1$, and captures the observation that a cell’s cytoskeleton takes some time to re-polarise (Ridley et al., 2003). In line with previous CPM-based models of cell migration (Vroomans et al., 2012; Vroomans et al., 2015) we set $τp=50$ MCS. Note that all cells have the same $τp$, but their initial moment of updating is randomised so that they do not update all at the same time.

### Chemotaxis

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Individual cells are able to migrate towards the perceived direction of a chemoattractant gradient. The slope of the gradient is very shallow, making it difficult to perceive the direction over the typical length of a cell. Moreover, several sources of noise are introduced: cell’s sampling error due to small size, noise due to integer approximation, and noise due to random absence of the signal.

The chemotactic signal is implemented as a collection of integer values on a second two dimensional lattice ($Λ2??2$, with the same dimensions as the CPM lattice). The (non-negative) value of a lattice site represents the local amount of chemotactic gradient. This value remains constant for the duration of one season ($τs$ MCS). The amount of chemotactic signal $χ$ is largest at the peak, which is located at the centre of one of the lattice boundaries, and from there decays linearly in all directions, forming a gradient: $χ?(d)=1+(kχ/100)?(L-d)$, where $kχ$ is a scaling constant, d is the Euclidean distance of a lattice site from the peak of the gradient, and L is the distance between the source of the gradient and the opposite lattice boundary; $L=|Λ1|$ for a square lattice. Non integer values of $χ$ are changed to $?χ?$ (the smallest integer larger than $χ$) with probability equal to $?χ?-χ$, otherwise they are truncated to $?χ?$ (the largest integer smaller than $χ$). Moreover, the value of $χ$ is set to zero with probability $pχ=0$ to create ”holes’ in the gradient. Setting $kχ=1$ and $pχ=0.1$ generates a shallow and noisy gradient. In a subset of simulations we used an alternative gradient, assumed to be generated by resources homogenously distributed on an entire side of the lattice, so that concentration isoclines are straight lines, see Appendix 2.4.

A cell has limited knowledge of the gradient, as it only perceives the chemotactic signal on the portion of $Λ2$ corresponding to the cell’s occupancy on $Λ1$. We define the vector $χ→?(c)$ as the vector that spans from the cell’s centre of mass to the centre of mass of the perceived gradient. Copies of lattice sites are favoured when they align with the direction of the vector $χ→?(c)$, i.e. when there is a small angle $θc$ between $χ→?(c)$ and the vector that spans from the centre of mass of the cell to the lattice site into which copying is attempted (Figure 1):

${Y}_{\text{chemotaxis}}=-{\mu }_{\chi }?\mathrm{cos}?\left({\theta }_{c}\right)$

where $μχ$ is the maximal propensity to move along the perceived gradient, and is set to $μχ=1$ in line with previous studies on cell migration (Vroomans et al., 2012) (chemotactic behaviour is robust to changes in $μχ$ however, see Appendix 1.6). A uniform random $θc∈[0,2π]$ is chosen whenever $|χ→?(c)|=0$, that?is, when, locally, there is no gradient (which may happen for very shallow gradients).

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In a subset of simulations we implemented an alternative mechanism of collective chemotaxis (proposed by Camley et al., 2016) that does no rely on individual cells’ gradient sensing. The mechanism works by combining three elements: cell–cell adhesion, contact inhibition of locomotion (Mayor and Carmona-Fontaine, 2010) and larger cell polarisation with higher concentration of the chemoattractant. The implementation of this mechanism in the CPM is straightforward. cell–cell adhesion was kept the same as explained above, and the chemoattractant is distributed to form the same gradient as in the previous paragraph. Every MCS each cell measures the average concentration of chemoattractant over the surface it covers $χ?(c)$ (note that this is a scalar). Then, in the copy biases Y we substitute a new term $YCIL$ to the term $Ychemotaxis$, with:

${Y}_{\text{CIL}}={\mu }_{{}_{\text{CIL}}}?\chi ?\left(c\right)$

and

${\mu }_{{}_{\text{CIL}}}=\left\{\begin{array}{cc}-3\hfill & \text{if cell attempts spin copy into medium}\hfill \\ 0\hfill & \text{if cell attempts spin copy with another cell}\hfill \\ 3\hfill & \text{if medium attempts spin copy into cell}\hfill \end{array}$

This definition of $μCIL$ introduces contact inhibition of locomotion by decreasing the probability that cells copy into each other, and increasing the probability that cells copy into medium.

### Evolutionary dynamics

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A population of N cells undergoes the cell dynamics described above for the duration of a season, i.e. $τs$ MCS. At the end of the season the evolutionary dynamics take place. The evolutionary dynamics are decoupled from the cell dynamics for the sake of simplicity, and consist of fitness evaluation, cell replication with mutation, and cell death to enforce constant population size. The evolutionary experiments last 400 seasons - that?is, 400 cycles of mutation-selection-dynamics. This value is larger than the time to reach evolutionary steady state in all simulations. Changes in $τs$ result in qualitatively different evolutionary dynamics, as reported in the main text.

### Fitness evaluation

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Fitness, that?is, the probability of replication, is calculated at the end of each season for each cell. We do not include any explicit advantage or disadvantage due to multicellularity, and instead assume that fitness is based only on individual properties of the cells. Therefore, any multicellular behaviour is entirely emergent in this simulation.

The fitness $F?(c)$ of a cell $c∈C$ depends only on the distance $d=d?(c)$ of the centre of mass of a cell c from the peak of the gradient as a sigmoid function which is maximal when $d=0$, and decreases rapidly for larger values of d:

$F?\left(c\right)=\frac{1}{1+{\left(\frac{{h}_{d}}{d}\right)}^{2}}$

with hd being the distance at which $F?(c)=1/2$.

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In a subset of simulations (see Appendix 4.1) we include a fitness penalty due to the metabolic costs of maintaining adhesion with other cells. We compute the average amount of boundary a cell has in contact with other cells over the course of a season $?m?$. The fitness of a cell $F?(c)$ at the end of the season is then multiplied by a decreasing function of $?m?$. For simplicity we use a linear function: $1-cm??m?$, with cm the metabolic cost of adhesion, which can vary in $[0,1]$. With small costs ($cm～0$) there is little penalty associated?with adhering, whereas with large costs ($cm～1$) the fitness penalty punishes adhering cells more severely than non-adhering ones. When $cm=1$, a cell that spent the entire season completely surrounded by other cell has fitness 0, that?is, it will not reproduce.

### Replication

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For each cell $i∈C$ with fitness $F?(i)$, the probability of replicating is $P?(cell ?i? replicates)=F?(i)/∑c?∈?CF?(c)$. We allow for N replication events, each calculated with the same probabilities, choosing only cells that were already present in the previous season (so not their offspring). Cells with larger fitness may be chosen multiple times for replication.

Each replicating cell divides along its short axis (see e.g. Hogeweg, 2000), ensuring that related cells start close to each other at the beginning of the new season. One of the two daughter cells, chosen randomly, can re-enter the competition for replication. All the lattice sites belonging to the other daughter cell are assigned a new (unique) spin value and the cell can mutate its receptor and ligand. The bitstrings of the receptor and ligand may be mutated with a per-position probability $μR,I$. Mutations flip individual bits (from 0 to 1, and vice versa).

Because repeatedly halving a cell’s area would quickly lead to very small cells, we run a small number $η$ of steps of the cell dynamics (without cell migration and chemotaxis) between two replication events that affect the same cell, so that cells can grow back to target size ($η=5$ MCS suffices).

### Death

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After replication, there are 2N cells on the lattice. In order to restore the initial population size N, half of the cells are removed from the lattice at random. When the initial population size is restored, the season ends. The new season begins by randomly placing the peak of a new gradient at the mid-way point of a randomly chosen boundary (different from the previous one). The remaining cells will then undergo the cell dynamics for the following $τs$ MCS.

## Appendix 1

### Collective integration of spatial information drives the evolution of multicellularity

#### Collective chemotaxis

##### 1.1 Diffusive exponent approximation

Main text Figure 3d shows the diffusion exponent for adhering and single cells in a cluster. Here we show how the figure is generated. In general, it is known that estimating single diffusion exponents from mean square displacement plots of anomalous diffusion is challenging (Kepten et al., 2015). Moreover, the diffusion exponent changes at different time interval. We therefore estimated the derivative of the log-log mean square displacement plot after interpolation with a polynomial, as shown in Appendix 1—figure 1.

Appendix 1—figure 1

### 1.2 Saturation of cluster speed with respect to cluster size

Appendix 1—figure 2 shows that a sublinear and saturating increase of the average speed of a cluster of cells for larger number of cells in the cluster. The average speed of the cluster is obtained through linear fit of the displacement/time plot, where displacement is measured as the movement of the centre of mass of the cluster towards the peak of the gradient.

Appendix 1—figure 2

### 1.3 Indistinguishable relative movement of cells with and without a chemotactic gradient

Here we investigate whether cells in a cluster move differently when they are performing chemotaxis or not. Appendix 1—figure 3 shows the flow field around moving cells in a cluster with or without a gradient, as devised by Szabó et al., 2010. In short, the flow field is calculated by taking each cell as a reference, and then rotating all other cells and their displacement vectors such that the reference cell displacement points to the right ($d→=[x0]$). Then the rotated displacement vectors are summed in bins at defined points in the neighbourhood (using all the cells as a reference, and using different time points) to obtain the average displacements in the neighbourhood (Szabó et al., 2010). In this case, the flow field shows that the relative movement of cells in a cluster is the same whether there is a gradient or not.

Appendix 1—figure 3

### 1.4?Chemotaxis with short persistence of migration and small persistence strength

Appendix 1—figure 4 shows that chemotaxis occurs in a rigid cluster of strongly adhering cells, albeit at a slower speed than with default parameter (cf. main text Figure 3a). The lower persistence strength reduces the number of changes in the relative position of cells within the cluster.

Appendix 1—figure 4

### 1.5?Robustness of collective chemotaxis to changes in persistence strength

Persistent migration positively affects collective chemotaxis. Appendix 1—figure 5 shows that this results (main text Figure 3) is robust to changes in the values of $μp$, provided that $μp$ is sufficiently larger than zero and not excessively large. When $μp～0$ persistent migration does not have a large effect on chemotaxis, and the chemotactic advantage of clusters is less pronounced. When $μp>5$ the model begins to break down, as the contribution of persistent migration to the energy function is too large compared to the other energies, and aberrant behaviour ensues (cells move forever in one direction regardless of adhesion or chemotaxis).

Appendix 1—figure 5

### 1.6?Robustness of collective chemotaxis to changes in chemotactic strength

Results presented in Figure 3 are qualitatively unchanged (and quantitatively largely unaffected) by changes in chemotactic strength $μχ$, as shown in Appendix 1—figure 6.

Appendix 1—figure 6

### 1.7?Chemotaxis of cells with different $AT$

We explored the behaviour of different cell sizes and cell number by running simulations where the total area of the cells is kept constant, $N?AT=5000$. We expect that large cells move with greater persistence towards the peak of the gradient than small cells, because they perceive a larger portion of the gradient, thus averaging out noise. Indeed, Appendix 1—figure 7 shows that larger cells perform chemotaxis more efficiently than smaller cells, given the same chemotactic gradient.

Appendix 1—figure 7

### 1.8?Extraction of straight segments from cell tracks

For the contour plots in Figure 3 of the main text, we extracted straight segments of the cells’ trajectories, then measured the length of this segment and its angle with the direction of the source of the gradient. To identify these straight segments, we take increasingly longer intervals between the recorded cell positions, and measure how far the intermediate data points are positioned from the line spanning these two data points (Appendix 1—figure 8A). As soon as one of the data points has a distance greater than a threshold, we stop extending the interval and continue from the cell position at which the chosen segment ends (the threshold value is set to three lattice sites; this value is chosen because it is the largest integer smaller than the average cell radius, given a cell area?=?50 lattice sites). In Appendix 1—figure 8B, the resulting segments are superimposed on cell position data from two simulations: one with a single cell and one with a cluster of adhering cells. While the overlap between the segment and the track itself varies, the length and orientation of the straight parts of the track are generally well-preserved in the segments.

Appendix 1—figure 8

## Appendix 2

### Evolution

#### 2.1?Adhesion strength distribution for $????=??????×??????$ MCS

We collected ligands and receptors for all cells over 10 generations after evolutionary steady is reached, and we calculated for every cell the contact energy with the medium ($J?(c1,m)$) and with all other cells ($J?(c1,c2)$) within the season in which it lived. A cell c1 adheres to a second cell c2 if their surface tension relative to medium (Glazier and Graner, 1993) is positive: $γc1,c2=J?(c1,m)-J?(c1,c2)/2>0$. We compared the resulting distribution with the distribution of $γ$ values obtained by generating 105 pairs of ligand and receptors, and calculating $γ$ from them. Appendix 2—figure 1 (blue) shows that random cells, on average, are neither adhering nor non-adhering (the calculated $?γ?$ was 0.00). Appendix 2—figure 1 (black) shows the distribution of all vs. all cells adhesion energy. Cells adhere to one another strongly ($?γ?=4.85$), and with little variability when compared to the random ligands and receptors (blue): the calculated variance is respectively (4.95 and 10.95) This method however neglects that cells self-organise within a season to minimise contact energy. We therefore extracted the adhesion energies of cells that are in contact with one another during a season and repeated the procedure outlined above. Appendix 2—figure 1 (red) shows that the surface tension distribution for observed contacts is on average higher and more peaked than the all vs. all distribution ($?γ?=5.82$ and variance 3.54).

Appendix 2—figure 1

#### 2.2?Evolution of multicellularity with alternative values of persistent migration strength

We tested the robustness of the evolution of multicellularity when the value of persistent migration strength was changed in the interval $μp∈[1,5]$. Because the evolution of multicellularity relies on collective chemotaxis, which in turn is sped up by larger values of $μp$, we expect that multicellularity evolves at smaller $τs$ when $μp$ is larger. Appendix 2—figure 2 shows that this is indeed the case. Moreover, it shows that $μp=1$, where collective chemotaxis is slow, multicellularity evolves only if $τs>250×103$ MCS (we tested $τs=500×103$ MCS, which is at the limit of computational feasibility). For $μp=5$ collective chemotaxis is so fast that no uni-cellular strategy was found (the smallest value tested was $τs=1×103$ MCS).

Appendix 2—figure 2

#### 2.3?Evolution of multicellularity with alternative values of persistent migration strength

. We tested the robustness of the evolution of multicellularity when the value of chemotactic strength was changed in the interval $μχ∈[0.5,2]$. In general, results were robust in this interval: multicellularity evolves during sufficiently long seasons ($τs$), and otherwise the uni-cellular strategy evolves (Appendix 2—figure 3). We found that the transition to multicellularity happens at longer seasons for larger values of $μχ$, despite the cluster moving faster (see Appendix 1—figure 6). We hypothesise that this is due to increased chemotaxis efficiency of individual cells at larger $μχ$.

Appendix 2—figure 3

#### 2.4?Evolution of multicellularity with a 1D gradient

Throughout the main text, the chemoattractant gradient decreases radially from the point where resources are assumed to be located. As cells migrate closer to the peak of the gradient, chemoattractant concentration isoclines are more curved, meaning that the direction of the gradient is progressively more precise, and thus chemotaxis may speed up. We tested if results are robust to modifying the gradient so that isoclines are straight and parallel. We ran a series of evolutionary simulations assuming that resources are distributed on the entire side of the lattice (instead of only on one point), so that chemoattractant concentration isoclines are straight lines, and we let the side change randomly every season. The numerical value of chemoattractant concentration at any point is given by the same function as for the radial gradient (see Materials and methods) $χ?(d)=1+(kχ/100)?(L-d)$, where d is here the distance of the point from the side of the lattice where the concentration is largest. Fitness is then evaluated based on the distance from the side of the lattice, with the same fitness function as in Materials and methods ($F?(d)$, where d is the distance of the centre of mass of the cell from the side of the lattice where the concentration is largest).

Results were largely unaffected by this change (Appendix 2—figure 4). Multicellularity evolves for $τs>50$, which is only a slightly longer seasons duration than in main text Figure 5.

Appendix 2—figure 4

#### 2.5?Evolution of multicellularity with a steep noiseless gradient

In the main text, the gradient is chosen such that individual cells perform chemotaxis inefficiently, as this provides a selectable incentive to aggregate when seasons are sufficiently long. When seasons are short, a uni-cellular strategy evolves where cells undergo fast dispersal after replication. Here we test the robustness of these results when the gradient is very steep, that?is, $kχ=10$, and noiseless, $pχ=0=0$. We found that multicellularity evolves at much shorter seasons ($τs≥5×103$ MCS) than in main text (Appendix 2—figure 5). This is because collective chemotaxis is faster than individual chemotaxis, and because the uni-cellular strategy is slower than chemotaxis when the gradient information is precise.

Appendix 2—figure 5

## Appendix 3

### Alternative chemotaxis mechanism

#### 3.1?Evolution of multicellularity when a cell cluster - but not individual cells - can perform chemotaxis

In the main text, we showed that the emergent organisation of a cluster of adhering cells increases the efficiency of chemotaxis and can be exploited as an indirect selection pressure for evolving adhesion when seasons are sufficiently long. Main text results are based on the assumption that individual cells are able to perform chemotaxis, albeit inefficiently. Here show that relaxing this assumption does not change results. To this end, we implemented a model developed by Camley et al., 2016 where collective chemotaxis emerges from the combination of contact inhibition of locomotion and stronger cell membrane polarisation when the chemoattractant concentration is larger. Because individual cells can only sense the concentration of the signal (and does not perceive the direction of the gradient), chemotaxis can only emerge as a property of a cell cluster. We modified the energy function by replacing the chemotaxis term with a new term that favours extension of cells into the medium in a concentration dependent manner and disfavours extensions towards other cells (with strength $μCIL$, see Materials and methods Section).

In Appendix 3—figure 1a we show that individual cells do not sense the gradient and therefore perform random walks, while adhering clusters move efficiently up the gradient through emergent chemotaxis. In Appendix 3—figure 1b we show that this alternative form of collective behaviour leads to similar results as in main text: multicellularity evolves for sufficiently large values of season duration $τs$, and a uni-cellular strategy based on dispersal evolves for shorter season duration.

Appendix 3—figure 1

## Appendix 4

### Evolution with cost

#### 4.1?The effect of costly adhesion on the evolution of multicellularity

In the main text, we showed that the indirect benefit of collective chemotaxis is sufficient to select for multicellularity when seasons are sufficiently long, and otherwise a uni-cellular strategy evolves. Here we explicitly incorporate costs for multicellularity. We assume that a metabolic cost is paid to maintain adhesion between cells. In one MCS, a cell incurs a cost proportional to the fraction of its boundary that is in contact with other cells $cm??m?$. This cost is summed for the duration of a season, so that cells that spend a long time in contact with other cells incur a larger metabolic cost. At the end of the season, a fitness penalty proportional to the overall (per-season) cost of adhesion is applied to each cell. For $cm=0$ this model reduces to the main text model, i.e. there are no costs for adhering with other cells. The maximum cost of adhesion is scaled to $cm=1$; in this case, a cell that is completely surrounded by other cells for the entire duration of a season (i.e. $?m?=1$) will not reproduce at all - effectively making dispersal the only viable strategy. Overall, this formulation of costs decreases fitness for cells that spend more time in contact with others. See main text Materials and methods for details.

We studied the evolution of multicellularity with costly adhesion for several value of the metabolic cost $cm∈[0,0.75]$, over a range of season duration $τs∈[10,100]3$ MCS. Results are shown in Appendix 4—figure 1. Results are identical to main text Figure 5 when costs are small ($cm<0.01$): multicellularity evolves when $τs≤50×103$ MCS. With larger costs $0.1≤cm≤0.5$, the evolution of multicellularity is shifted to larger season duration. Only when costs are sufficiently large ($cm=0.75$), multicellularity did not evolve over the values of $τs$ that we tested. Altogether, the evolution of multicellularity for longer season duration is a robust outcome of the model, provided that the cost of adhesion is not too high.

Appendix 4—figure 1

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## Decision letter

1. Raymond E Goldstein
Reviewing Editor; University of Cambridge, United Kingdom
2. Aleksandra M Walczak
Senior Editor; école Normale Supérieure, France

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work is a computational work on the evolution of multicellularity. Inspired by the life cycle of Dictyostelium, the authors develop a mathematical model that incorporates cellular aggregation and chemotaxis in a cyclic environment and show how the greater fidelity of multicellular chemotaxis leads to selection for that state. The model is an enlightening case study of this very important evolutionary issue.

Decision letter after peer review:

Thank you for submitting your article "Evolution of multicellularity by collective integration of spatial information" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Aleksandra Walczak as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (http://www.asadoresteatre.com/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

In this paper, the authors consider the problem of evolutionary transitions to multicellularity, and in particular the case in which aggregation drives the process. Inspired by the life cycle of Dictyostelium, they consider a model in which cells (moving on a grid) search for resources and can adhere to each other based on the match between ligand and receptors on their surfaces. All of this takes place in the context of a chemotactic march towards a local chemoattractant within one temporal "season", after which fitness-dependent reproduction occurs, the population is culled back to its starting size, and the environmental conditions are changed.

The reviewers all are of the opinion that this work provides an interesting perspective on a possible mechanistic basis of 'collective-level' function, that stems from physical interactions among cells in the absence of explicitly modelled costs and benefits of single cell's choices. At the same time, the reviewers were clear that there are many aspects of the model and the modelling approach that are not clear, unnecessarily complicated or not well justified. In light of these, major revisions to the paper will be necessary, as explained below.

Essential revisions:

1) Considering the paper as a whole, there are far too many things happening at once to draw any meaningful conclusions. There is the complexity of adhesion, the nature of the chemotaxis, the temporal switching between seasons, and the reproduction process. Each of these is explored to a limited extent, and it is unclear which are absolutely crucial to the conclusions reached and how sensitive the conclusions are to the assumptions made.

2) Regarding the definition of the model itself, the reviewers find it inappropriate to relegate so much of that explanation to the Materials and methods section. The very large number of parameters (18) in Table 1 needs to be made clear (and that table should be referenced – it does not appear to be at present). Please explain more of the model in the body of the paper.

3) The reviewers are supportive of abstract models, but inasmuch as the authors have set up a physical/biological scenario with familiar processes (chemotaxis, adhesion) it would have been very helpful to have justified the kinds of dimensionless parameters that characterize the model in terms of real physical and biological features.

4) The essence of a Monte Carlo simulation is the definition of an energy function and a temperature, which together yield a Boltzmann factor that is used to decide if an attempted step is taken. The authors do not make clear in the main body of the text that they are performing a Monte Carlo calculation (that is only specified in the Materials and methods section). They refer to MCS (Monte Carlo Steps) in the body of the paper without defining that term. But the larger question is why this kind of nonequilibrium biological system should have such an energy, and what would be the biological significance of the temperature? In addition, of course, the "steps" taken are those of Monte Carlo algorithm and have no direct interpretation in terms of real time.

5) The presentation of the model and the main results lack clarity in some key aspects:

a) the relation between cell–cell and cell-medium adhesion and surface tension (subsection “Strongly adhering cells perform efficient collective chemotaxis We first assessed how well groups of cells with different adhesion strengths”) is not explained, so it is not really clear what negative surface tension means.

b) as surface tension pools two different kinds of adhesion, does it mean that in a certain sense adhesion to the surface can be traded off against adhesion between cells? This is important to know in connection to experiments.

c) since the measure of sequence complementarity is symmetric, why does one need to suppose the existence of both a ligand and a receptor? Would it change anything if cells were characterized by only one sequence? If yes, it would be interesting to know if at the end of the numerical experiment ligand and receptor evolve to be the same or if 'molecular' diversity is maintained.

d) the process of cell division/regrowth and the fact that cells do not change position from one season to the next should be more clearly explained in the main text.

e) what is the initial spatial distribution of cells at the beginning of every season, and if this matters (many models assume aggregation-dispersal cycles, that does not seem to be the case here), should be specified or repeated in the evolutionary section.

f) Figure 5 should depict a case of bistability: now it is not clear that different evolutionary outcomes are associated with differences in the initial surface tension, rather than in the initial cell configuration. It would by the way be interesting to see if the second also gives rise to bistability.

6) Cell migration (subsection “Cell migration”) is defined in terms of the actual direction of the cell over the past steps. This seems to build in persistence, and would appear to have a profound effect on the dynamics. Is this the case?

7) In general, it would be useful if statements like "In our case, aggregation leads to a highly efficient search strategy, guided by long-range, albeit noisy, gradients." (Discussion section) could be made more quantitative. For instance, one would like to get a sense of whether the conclusions are robust to changes in (at least a few important) parameters. One would expect so from results in active matter physics, but it would be useful of the authors could argument it and indicate when they expect different conclusions to hold. Moreover, what is the role of the particular gradient chosen here in 'focalizing' the formation of multicellular groups (would an essentially 1-D gradient, where isolines are parallel, do the job?) and of its intensity/spatial variation (in the movie, one sees that the centre of the gradient changes among four positions, does it matter?).

8) The authors claim that, in contrast to previous work, the increased fitness of the aggregates (better ability to perform chemotaxis) is an emergent property. The reviewers struggled to find a physical/mathematical explanation as to why such a relationship exists in the model but it appears that subsection “Chemotaxis” contains the mechanism. The text speaks of the "centre of mass of the perceived gradient". Unless we are mistaken, such a quantity averages over the individual constituent's contributions in such a way that larger cells will have more accurate measurements of the gradient. This is just the law of large numbers. If this is the case, then this feature is not an emergent property at all, but is part of the definition of the model. Please clarify. If the above critique is correct, then why bother with the complex model? The authors could just use the fact that larger aggregates are better at chemotaxis for the reason given and proceed from there.

The above suggests that the authors have basically put the answer in from the beginning. The model has the explicit feature that those that perform chemotaxis better reproduce more. So of course, that will be reinforced. But multicellularity has costs and benefits, and the model does not appear to contain any costs associated with multicellularity. In real biological examples there are many – the increased metabolic cost of the structures that hold cells together, greater need for regulatory genetic networks, etc.

9) The referencing of the text to Figure 3 is all mixed up, leaving both text and figure hard to follow. -The authors should revise this section and make sure that they clearly state if higher chemotactic performance arises due to longer persistence of cell clusters only or due to longer persistence and higher chemotactic accuracy of whole cell cluster. Varennes et al., (2017) and manuscripts citing this work give measures for chemotactic accuracy within cell populations. – Figure 3D should show error bars. Annotation of Figure 3F should be detailed, what is bar{X}? Is this the local gradient including noise or averaged on which scale.

10) The assessment time scale emerges as a decisive factor – it appears as a theoretical construct right now. What could it correspond to in the real world? Please discuss.

11) As for the particular details of the model, it is left unsaid in the main text but stated in the Materials and methods section that there is a preferred cell size A_T and a harmonic energy around that size. As the target size is (Table 1) some 50 pixels, we are confused, as it seems that each "cell" occupies one lattice size. This energy would then clearly bias the system to aggregate already. Please clarify. The use of the term "pixel" for a lattice site is confusing.

12) The literature overview appears limited – please revise and consider recent work for example but not limited to Varennes et al., (2017); Jacobeen et al., (2018). The authors should also discuss Guttal and Couzin, 2010. And they should acknowledge relevant literature exploring, for example, similar issues in the Volvocales; Solari er al., (2006); Solari, Kessler and Goldstein, (2013).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Evolution of multicellularity by collective integration of spatial information" for consideration by eLife. We regret that delay in reaching a decision on your revised manuscript, due in part to challenges presented by the pandemic. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Aleksandra Walczak as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (http://www.asadoresteatre.com/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

In this paper, the authors consider the problem of evolutionary transitions to multicellularity, and in particular the case in which aggregation drives the process. Inspired by the life cycle of Dictyostelium, they consider a model in which cells (moving on a grid) search for resources and can adhere to each other based on the match between ligand and receptors on their surfaces. All of this takes place in the context of a chemotactic march towards a local chemoattractant within one temporal "season", after which fitness-dependent reproduction occurs, the population is culled back to its starting size, and the environmental conditions are changed.

Essential revisions:

The authors have significantly reshaped the manuscript and added new interesting simulations in response to the reviewers' comments. We think the sensitivity analysis to different parameters, as well as the tests with alternative models, is important in showing the generality of the results.

If the authors made considerable efforts in explaining the model hypotheses, we found ourselves still puzzled about a few points in the main text, and reading the methods only provided part of the answers. We think some corrections are needed, in particular to help the reader understand how and when clustering of ameboid cells enhances chemotaxis.

1) The multiple scales at which different properties are defined makes it still difficult to figure the model out. Definition of the cell-to-cell contact energy J_{c,c} (subsection “2.1 Model setup”) and of averages in Figure 3 would help. The transition between the site and cell scale seems to be problematic if more than one cell have the same identifying string, which would happen if mutations do not happen (subsection “4.2 Evolutionary dynamics”), or if connectedness within cells is not ensured (subsection “4.1 Cell dynamics”).

2) We do not see how negative surface tension may imply 'repulsion' (subsection “Evolutionary model”, subsection “The evolution of uni- or multicellular strategies depends on environment stability”, Discussion section) between cells, rather than just an average higher probability for sites at the cell surface of sticking to the medium than to other cells. 'dispersion', also, may be due to amplification of fluctuations by persistence on the time scale of cell velocity update. Description of the behaviour of cells in isolation, especially how cell displacement depends on the magnitude of negative gamma would be very useful.

3) We do not understand the explanation for the evolution of small cell-to-cell adhesion for high frequency of environmental change. The authors claim that clusters always migrate faster up the gradient than single cells (Discussion section), but then in subsection “The evolution of uni- or multicellular strategies depends on environment stability” they seem to indicate the opposite. In the present formulation it is not clear if the advantage of single cells is given by the growing importance of the transient to clustering after reproduction and culling (that we imagine introduces, willing or not, a sort of local dispersal by creating 'holes' in coherent clusters), or by the fact that moving fast in one direction might not be the best strategy when such direction changes very fast (alike environmental response vs bet-hedging).

4) We wonder if the fact that the string defining adhesion to the medium is a substring of that defining adhesion to other cells may have evolutionary effects, as the two kinds of adhesion will be in general correlated. We understand that this is a convenient choice, but are not sure that the existence of such correlations may be justified for cells. Secondly, by looking at Appendix Figure A2.1, we are puzzled by the statement that cell–cell variation in adhesion strength after evolution is small (subsection “The evolution of uni- or multicellular strategies depends on environment stability” and Appendix subsection “2.1 Adhesion strength distribution for τs = 100 x 103 MCS”), since no quantitative comparison is made with other situations (for instance, when the unicellular strategy evolves?).

https://doi.org/10.7554/eLife.56349.sa1

## Author response

Summary:

In this paper, the authors consider the problem of evolutionary transitions to multicellularity, and in particular the case in which aggregation drives the process. Inspired by the life cycle of Dictyostelium, they consider a model in which cells (moving on a grid) search for resources and can adhere to each other based on the match between ligand and receptors on their surfaces. All of this takes place in the context of a chemotactic march towards a local chemoattractant within one temporal "season", after which fitness-dependent reproduction occurs, the population is culled back to its starting size, and the environmental conditions are changed.

The reviewers all are of the opinion that this work provides an interesting perspective on a possible mechanistic basis of 'collective-level' function, that stems from physical interactions among cells in the absence of explicitly modelled costs and benefits of single cell's choices. At the same time, the reviewers were clear that there are many aspects of the model and the modelling approach that are not clear, unnecessarily complicated or not well justified. In light of these, major revisions to the paper will be necessary, as explained below.

We are glad that reviewers found our work interesting – the summary nicely captures the message of the paper. We extensively revised the manuscript after the reviewers comments and suggestions.

The largest changes to the manuscript are summarised here, and below we respond to reviewers remarks point by point.

We have greatly expanded the model specification in the main text, and explain the model in much more detail in the Materials and methods section – including a clarification of parameter values. We have also run a large number of new experiments, added a robustness analysis of both collective chemotaxis and the evolutionary dynamics, we checked that our results hold when a different model of emergent chemotaxis is implemented and when costs are added. Finally, we discuss our results more in depth.

Essential revisions:

1) Considering the paper as a whole, there are far too many things happening at once to draw any meaningful conclusions. There is the complexity of adhesion, the nature of the chemotaxis, the temporal switching between seasons, and the reproduction process. Each of these is explored to a limited extent, and it is unclear which are absolutely crucial to the conclusions reached and how sensitive the conclusions are to the assumptions made.

Although the model is somewhat complex in the implementation details, its key ingredients are – as the reviewers note – just four: (evolvable) adhesion, chemotaxis, environmental switching and replication. Conceptually, these are the minimal elements for a mechanistic evolutionary model: the adhesion properties are inherited with mutation upon replication, and the environment switch after reproduction allows for continual selection on reaching the peak of the gradient by chemotaxis.

We now explicitly mention these four elements in the Introduction.

We revised and expanded the model definition in the main text (subsection “2.1 Model setup”) and the Model section (see also point 2) and ran several additional simulations to show that results are robust to parameter changes and to some structural changes to the model (see Point 5 and 7 for details).

We also expanded the literature reviewed in the Discussion section and revised the Introduction to better motivate the approach we used for our model.

2) Regarding the definition of the model itself, the reviewers find it inappropriate to relegate so much of that explanation to the Materials and methods section. The very large number of parameters (18) in Table 1 needs to be made clear (and that table should be referenced – it does not appear to be at present). Please explain more of the model in the body of the paper.

We expanded the model definition in the main text and reworded it to make the principles of the model clearer. We tried to strike a better balance between giving enough details and maintaining the exposition of the model descriptive enough for a more intuitive understanding. The parameter table is now referenced in the main text (subsection “2.1 Model setup”) and in the Materials and methods section and the parameters referenced in the table are introduced in the main text.

3) The reviewers are supportive of abstract models, but inasmuch as the authors have set up a physical/biological scenario with familiar processes (chemotaxis, adhesion) it would have been very helpful to have justified the kinds of dimensionless parameters that characterize the model in terms of real physical and biological features.

The reviewers are correct that our system incorporates some biophysical elements into a simple evolutionary model. The values of some parameters, such as those for migration, are taken from published literature where the Cellular Potts Model is calibrated against empirically determined eukaryotic cell behaviour; other parameters, e.g. those of cell adhesion and volume constraint, do not have a direct correspondence to a biophysical property of the cell, but are a coarse-grained description of some cell properties. Therefore, their value cannot be directly taken from experiments, but is instead inferred from model behaviour and is in scaling relationships with other parameters. For the “temperature” parameter please see point 4). We revised the Materials and methods section in all the paragraphs where parameters are introduced to give the details of the numerical values of the parameters: we added references when these values are taken from literature, and motivate our choices for the other parameters. We also added references that explain both the connection of these macroscopic parameters to the biophysics of the cell and their scaling relationships.

4) The essence of a Monte Carlo simulation is the definition of an energy function and a temperature, which together yield a Boltzmann factor that is used to decide if an attempted step is taken. The authors do not make clear in the main body of the text that they are performing a Monte Carlo calculation (that is only specified in Subsection “4 Model”). They refer to MCS (Monte Carlo Steps) in the body of the paper without defining that term. But the larger question is why this kind of nonequilibrium biological system should have such an energy, and what would be the biological significance of the temperature? In addition, of course, the "steps" taken are those of Monte Carlo algorithm and have no direct interpretation in terms of real time.

We now explicitly state that we specifically use the Metropolis algorithm (which is a Monte Carlo method) and the rationale for the energy function in the main text (subsection 2.1 Model setup”). We also define a Monte Carlo Step (subsection 2.1 Model setup”).

With the intention of making the paper accessible to experimental researchers who might be less interested in the mathematical details, we decided to leave the formal description of the model for the Materials and methods section.

Regarding the particular choice of the energy function: it is an extension of the typical Hamiltonian used in the Cellular Potts Model, but we believe that alternative formulations would work just as well, provided that they incorporate adhesion, cell size and chemotaxis. Formulating the model in terms of its free energy makes it straightforward to extend it by adding an energy term for each biological phenomenon under study, but the basic CPM model has been shown to be mathematically equivalent to a model where overdamped Newtonian forces are specified: the energy minimization takes care of balancing the various forces acting on the cell (adhesion, volume conservation, migration, etc…) (Magno et al., 2015).

The “Boltzmann temperature” term introduces noise, and it is related to the probability that energetically unfavourable fluctuations occur. Thus, temperature scales the statistical mechanics of the model, but does not have direct biological significance.

When one is interested in finding the equilibrium state of a system, Monte Carlo Steps do not have to correspond to units of time. In our case, however, the steps of the algorithm have physical significance, because they correspond to changes caused by the forces acting on a cell. This, together with using the Metropolis algorithm and a low enough temperature, guarantees that every Monte Carlo step lasts effectively the same amount of physical time in our model, so that we can use the model for dynamical simulations (see Glazier, Balter and Poplawski, 2007, and for a broader discussion e.g. Newman and Barkema 1999).

We now clarify the energy function and its different terms more extensively in the Materials and methods section, including a better explanation of the temperature parameter.

We also added references to literature that delves deeper in the statistical mechanics (subsection “4.1 Cell dymanics”) and the biophysical motivation for the construction of the various terms of the energy function.

5) The presentation of the model and the main results lack clarity in some key aspects:

a) the relation between cell–cell and cell-medium adhesion and surface tension (subsection “Strongly adhering cells perform efficient collective chemotaxis We first assessed how well groups of cells with different adhesion strengths”) is not explained, so it is not really clear what negative surface tension means.

We now explain the connection between adhesion and surface tension in the model introduction of the main text (subsection “2.1 Model setup”).

b) as surface tension pools two different kinds of adhesion, does it mean that in a certain sense adhesion to the surface can be traded off against adhesion between cells? This is important to know in connection to experiments.

Yes, in the sense that adhesion between cells can occur when cells form stronger junctions, or when they adhere less strongly to the medium.

We now mention this in the main text (subsection “2.1 Model setup”).

However, the medium in our model is not a substrate on top of which cells live. Modelling such substrate is possible (see e.g. van Oers et al., 2014), but would have further complicated the model.

c) since the measure of sequence complementarity is symmetric, why does one need to suppose the existence of both a ligand and a receptor? Would it change anything if cells were characterized by only one sequence? If yes, it would be interesting to know if at the end of the numerical experiment ligand and receptor evolve to be the same or if 'molecular' diversity is maintained.

Separate ligand and receptor are needed for versatility in cell adhesion. To see why, consider that with a one-sequence complementary binding system, two cells with identical sequence, such as two cells after division, would always strongly repel (or depending on the implementation, strongly adhere). Instead, with separate ligand and receptor, two identical cells can have any binding strength (also three cells may have any combination of adhesion strength, etc…).

This versatility might be possible also with a one-sequence system, if more complex rules were implemented. We chose these ones for the sake of simplicity, versatility and because they are a more general framework (which we are currently further developing) that allows differential adhesion between cell types.

We now make clear in the main text how the ligand-receptor system is important for versatility in adhesion (subsection “2.1 Model setup”).

Moreover, the reviewers comment prompted us to actually measure the diversity in adhesion energies when cells adhere. We now show that the distribution of adhesion energy is uni-modal, indicating that there is essentially one phenotype, and thus any molecular diversity that still allows for adhesion is selectively neutral, in agreement with the fact that we observe only one cluster at evolutionary steady state.

We now show this in Supplementary Material (Supp. Section S9) and refer to it in the main text (subsection “2.1 Model setup”).

d) the process of cell division/regrowth and the fact that cells do not change position from one season to the next should be more clearly explained in the main text.

We now clarify it in the main text (subsection “2.1 Model setup”)

e) what is the initial spatial distribution of cells at the beginning of every season, and if this matters (many models assume aggregation-dispersal cycles, that does not seem to be the case here), should be specified or repeated in the evolutionary section.

At the beginning of a new season cells retain their previous spatial distribution, i.e. we do not include dispersal. We now make this clearer in the main text both in the model introduction and in the evolutionary section (subsection “2.1 Model setup”).

f) Figure 5 should depict a case of bistability: now it is not clear that different evolutionary outcomes are associated with differences in the initial surface tension, rather than in the initial cell configuration. It would by the way be interesting to see if the second also gives rise to bistability.

Bistability depends on the initial surface tension in Figure 5 in the sense that we initialise a population of identical cells with a predetermined initial adhesion energy, and we let evolution proceed from there.

Within each season, both the spatial distribution and the surface tension of the resident population and the mutant are important, as they co-determine the direction of evolution. However, the spatial distribution of the cells in the evolutionary simulation is not a free parameter, but an outcome of the model dynamics.

This is what we aimed to show in Figure 6C and 6D.

We could, however, initialise an evolutionary experiment with a bimodal population, such as those in Figure 6A and B – which could correspond to a case where a population (rather than mutants) is invading the space of another. In that case, the initial configuration would be critical for the first season: the population that begins closer to the gradient wins. After this one season, the adhesion energy of the resident population will determine the evolutionary dynamics.

We revised the paragraph to make this clear (subsection “2.1 Model setup”), and along with this we changed the order of the figure, so that what was C,D (representing a situation more similar to the evolutionary dynamics) are now A,B and vice versa (the content of the figure is unchanged).

We agree with the reviewers that trying other initial cell configurations, such as dispersal, would be interesting: we make the case that bistability would still arise in the Discussion section.

6) Cell migration (subsection “Cell migration”) is defined in terms of the actual direction of the cell over the past steps. This seems to build in persistence, and would appear to have a profound effect on the dynamics. Is this the case?

Yes, short-term persistence is important for the collective dynamics. As cells keep their direction of motion for a short while (i.e. for their persistence time = 50 MCS), their pushing forces on each other are resolved when cells align and form streams within the cluster.

We now clarify the effect of persistence on collective behaviour in the subsection “2.1 Model setup” and Discussion section; where we also added some additional references. We also ran new simulations to check different values of persistence strength (see also remark 7).

This also ties into the improved chemotactic migration of clusters of cells and whether this is due to the law of large numbers (see reply to remark 8).

7) In general, it would be useful if statements like "In our case, aggregation leads to a highly efficient search strategy, guided by long-range, albeit noisy, gradients." (Discussion section) could be made more quantitative. For instance, one would like to get a sense of whether the conclusions are robust to changes in (at least a few important) parameters. One would expect so from results in active matter physics, but it would be useful of the authors could argument it and indicate when they expect different conclusions to hold. Moreover, what is the role of the particular gradient chosen here in 'focalizing' the formation of multicellular groups (would an essentially 1-D gradient, where isolines are parallel, do the job?) and of its intensity/spatial variation (in the movie, one sees that the centre of the gradient changes among four positions, does it matter?).

We removed the statement, and we revised the paragraph to make clearer what we meant, as we added some references (see point 12).

We also ran several simulations to assess robustness. We studied the robustness of individual vs. collective behaviour by varying chemotactic strength, persistent migration strength, cell size (which was already in the paper, but it is now clarified) and population size (for more on population size see point 9).

We find (and report in the main text – subsection “2.1 Model setup”, and Appendix 1 subsection “1.5 Robustness of collective chemotaxis to changes in persistence strength” and subsection “1.6 Robustness of collective chemotaxis to changes in chemotactic strength”) that, qualitatively, results are robust to migration parameter changes: collective chemotaxis is faster than single cell chemotaxis. Quantitatively, larger persistence strength increases the speed of collective chemotaxis more than chemotactic strength.

We then checked that the outcome of the evolutionary dynamics were also robust to changes in migration parameters. We find that it still holds that adhesion evolves when seasons are sufficiently long, and otherwise the single-cell strategy evolves.

We also ran evolutionary simulations with a 1D gradient (with parallel isoclines) and with a steeper gradient. Results change quantitatively but not qualitatively, with a larger change in the case of a steeper (more precise) gradient.

We report on this in the main text (subsection “2.1 Model setup”), and we have added Supplementary sections S10, S11 and S12, where we show the new data.

Lastly, the four positions of the peak of the gradient were chosen for visual clarity, and the evolutionary dynamics would be identical if peak location was chosen differently (because individual cells are large enough not to sense the lattice anisotropy, and thus it makes no difference where the gradient is).

8) The authors claim that, in contrast to previous work, the increased fitness of the aggregates (better ability to perform chemotaxis) is an emergent property. The reviewers struggled to find a physical/mathematical explanation as to why such a relationship exists in the model but it appears that subsection “Chemotaxis” contains the mechanism. The text speaks of the "centre of mass of the perceived gradient". Unless we are mistaken, such a quantity averages over the individual constituent's contributions in such a way that larger cells will have more accurate measurements of the gradient. This is just the law of large numbers. If this is the case, then this feature is not an emergent property at all, but is part of the definition of the model. Please clarify. If the above critique is correct, then why bother with the complex model? The authors could just use the fact that larger aggregates are better at chemotaxis for the reason given and proceed from there.

The above suggests that the authors have basically put the answer in from the beginning. The model has the explicit feature that those that perform chemotaxis better reproduce more. So of course, that will be reinforced. But multicellularity has costs and benefits, and the model does not appear to contain any costs associated with multicellularity. In real biological examples there are many – the increased metabolic cost of the structures that hold cells together, greater need for regulatory genetic networks, etc.

The reviewers raise two important points, that together form the main argument of the paper: Is collective behaviour really emergent or rather built-in? And if so, does it trivially follow that collective chemotaxis drives the evolution of multicellularity? They also point out that additional costs may be paid by cells in multicellular aggregates. We respond to these three points (collective chemotaxis, evolution, costs) in order.

The reviewers are correct that with our implementation of chemotaxis, larger cells will measure the gradient more accurately; we agree that chemotaxis of individual cells is not an emergent property, and that cluster chemotaxis results from chemotaxis of individual cells. However, chemotaxis of cell aggregates does not straightforwardly follow the law of large numbers in our model; in fact, we find that clusters of many small cells perform worse than a single large cell with the same total size (we mention this in subsection “2.1 Model setup”, and show it in Suppl. Section S7), and that chemotaxis scales sublinearly with of cluster size (which we now explain in the main text – subsection “2.1 Model setup” and show in Supp. Section S2).

This is because neither our cells nor our clusters are rigid, and there is no explicit mechanism for transferring gradient information between cells. It is therefore not immediately obvious that a larger number of cells have a “shared” knowledge of the gradient in the same way that one big cell has precise knowledge of all of the gradient it covers. In our model, cells convey gradient information through emergent collective streaming (see response to point 6, and earlier work by Smeets et al., 2016, Beltman et al., 2006, Szabo et al., 2006), which becomes biased towards the weak chemotactic signal. We now mention this in the results (subsection “2.1 Model setup”), add these references and come back to it in the Discussion section.

When cells form rigid clusters instead, collective migration is tantamount to the law of large numbers – and therefore also built-in (as assumed in e.g. Varennes et al., 2017) because a cell’s polarisation towards the perceived gradient translates linearly and instantaneously to cluster movements. We thank the reviewers for pointing this out to us, as it constitutes a good reference model to compare our results to.

To completely make sure that our results do not depend on the effect of the law of large numbers, we ran a few simulations in which we implemented an alternative mechanism of collective chemotaxis (proposed in Camley et al., 2016). With this mechanism individual cells do not sense the gradient, only the local concentration of the signal. This entailed a small modification of the Hamiltonian to introduce contact inhibition of locomotion (CIL) and higher membrane polarisation rate when exposed to higher concentration of the chemoattractant. Our evolutionary results are remarkably robust to the particular implementation of collective chemotaxis. We now describe this in subsection “2.1 Model setup” and show the data in Supplementary Section S14, as well as comment on this in the Discussion section.

Regarding the evolutionary dynamics:

We agree with the reviewer that in the experiment as we have set it up, potential selection of multicellularity could be expected a priori, because clusters of cells always perform chemotaxis better in our model. We write that we do not explicitly select for multicellularity because the model rewards cells for being in a group only implicitly. This is different from many previous models where such a term is explicitly present in the equation to calculate fitness (e.g. in terms of shared benefits or in terms of decreased chance of predation).

Instead, selection acts only individual cells to increase their reproductive success: i.e. some cells will be close to the source of the gradient and reproduce more, but this does not need to happen through collective behaviour – even though adhering aggregates are always better than individual cells at chemotaxis. Even if we were able to achieve full open-ended evolution in a mathematical model, it would always be possible to argue that the result has been put in 'a priori' if selection happens to stumble upon mechanisms that we knew existed a priori in another system. It would thus philosophically be impossible to formally claim the system has found a 'novel' solution. Interestingly, in our set up, collective chemotaxis does not evolve when seasons are short, and instead the opposite strategy is selected because it leads to fast scattering, so that some cells will have higher fitness.

This shows an important distinction between fitness function (which cells replicate) and the evolutionary strategy, i.e. how cells organise through evolution as a response to the fitness function.

We agree that this is a more nuanced point that wasn’t very clear in the manuscript, so we now explain this more extensively in the Discussion section.

Lastly, regarding the lack of costs in the model: we agree with the reviewers that multicellularity may come with costs. We originally did not include them to limit model complexity. However, we took up the reviewers’ suggestion and ran a set of simulations in which we incorporated a cost for adhering to other cells. Costs are as follows: cells that spend most of the season adhering to other cells receive a fitness penalty proportional to the amount of time and membrane in contact.

Results are robust to this change: long seasons lead to multicellularity, provided that costs are not too high – with the evolutionary transition to multicellularity shifted to longer season duration with larger costs. We now present these new results in the main text (subsection “2.1 Model setup"), and give more details about implementation and results in Supplementary Section S15.

9) The referencing of the text to Figure 3 is all mixed up, leaving both text and figure hard to follow. -The authors should revise this section and make sure that they clearly state if higher chemotactic performance arises due to longer persistence of cell clusters only or due to longer persistence and higher chemotactic accuracy of whole cell cluster. Varennes et al., (2017) and manuscripts citing this work give measures for chemotactic accuracy within cell populations. – Figure 3D should show error bars. Annotation of Figure 3F should be detailed, what is bar{X}? Is this the local gradient including noise or averaged on which scale.

We revised the paragraph to improve its clarity, and the references are now in order.

We now clarify that the higher performance of cells in a cluster relative to single cells comes about from collective streaming of these cells, which increases persistence and biases migration towards the peak of the gradient (see also responses to point 6 and 8).

Moreover, we report on the robustness analysis of chemotactic parameters (see response to point 7).

Regarding accuracy measures: we now use the model proposed in Varennes et al., (2017) as a reference model, to explain why our results differ (see also point 8).

We present this in the main text (subsection “2.1 Model setup"), and show it in the Supplementary Section S2.

Regarding error bars in Figure 3D: error bars around numerically inferred derivatives are a notoriously difficult problem, as data fluctuations become over-amplified. Diffusive exponent calculations – which rely on fitting a power law to the data (and thus allows error estimates), are also often inaccurate with anomalous diffusion.

To show more transparently that the plot represents the data accurately, we clarify the intermediate steps from the MSD plot (Figure 3C) to Figure 3C, in a new Supplementary Material section S1.

We improved the annotation of the Figure 3F (X is the “true” direction of the gradient (the direction from the centre of mass of the cell to the peak of the gradient), x is the direction perceived by individual cells).

10) The assessment time scale emerges as a decisive factor – it appears as a theoretical construct right now. What could it correspond to in the real world? Please discuss.

It corresponds to the stability/volatility of resources. The idea behind it is that resources that persist in one place for sufficiently long can be traced up their chemokine gradient (this selects for multicellular behaviour because a group does chemotaxis better than individuals). When resources are volatile and the associated gradient does not persist for long, a search strategy based on spreading out allows some cells to reach them (which selects for uni-cellular strategy).

Resources are modelled only implicitly – through the chemoattractant gradient they generate and through how long they persist.

The precise seasonality of these resources might be a good assumptions if resources are deposited in the system by periodic phenomena (e.g. tides, or yearly cycles), but might be unrealistic for some types of resources, and some stochasticity will be introduced in future work. However, we think that if the distribution of resource persistence is sufficiently well behaved (e.g. if its mean exists and it has a relatively small variance compared to the rate of cell evolution), then cell evolution would converge to the average season duration.

We now clarify what the season duration corresponds to in the main text (subsection “2.1 Model setup”) and discuss its limitations in the Discussion section.

11) As for the particular details of the model, it is left unsaid in the main text but stated in the Materials and methods section that there is a preferred cell size A_T and a harmonic energy around that size. As the target size is (Table 1) some 50 pixels, we are confused, as it seems that each "cell" occupies one lattice size. This energy would then clearly bias the system to aggregate already. Please clarify. The use of the term "pixel" for a lattice site is confusing.

We now clarify that cells occupy more than one lattice site in the main text (subsection “2.1 Model setup”). The energy term proportional to (A – A_T)^2 is indeed responsible for maintaining cell size around the value A_T = 50 lattice sites, by energetically dis-favouring cell size deviations from A_T (we now clarify this in subsection “2.1 Model setup”).

We also substituted the term lattice site to the term pixel throughout the text and figures.

12) The literature overview appears limited – please revise and consider recent work for example but not limited to Varennes et al., (2017); Jacobeen et al., (2018). The authors should also discuss Guttal and Couzin, 2010. And they should acknowledge relevant literature exploring, for example, similar issues in the Volvocales; Solari er al., (2006); Solari, Kessler and Goldstein, (2013).

We thank the reviewers for the additional references. They are now integrated in the Introduction and Discussion section.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Summary:

In this paper, the authors consider the problem of evolutionary transitions to multicellularity, and in particular the case in which aggregation drives the process. Inspired by the life cycle of Dictyostelium, they consider a model in which cells (moving on a grid) search for resources and can adhere to each other based on the match between ligand and receptors on their surfaces. All of this takes place in the context of a chemotactic march towards a local chemoattractant within one temporal "season", after which fitness-dependent reproduction occurs, the population is culled back to its starting size, and the environmental conditions are changed.

Essential revisions:

The authors have significantly reshaped the manuscript and added new interesting simulations in response to the reviewers' comments. We think the sensitivity analysis to different parameters, as well as the tests with alternative models, is important in showing the generality of the results.

If the authors made considerable efforts in explaining the model hypotheses, we found ourselves still puzzled about a few points in the main text, and reading the methods only provided part of the answers. We think some corrections are needed, in particular to help the reader understand how and when clustering of ameboid cells enhances chemotaxis.

We have added clarifications and more details in several parts of the paper, we have also included two new videos taken from a simulation with one cell and a simulation with a population of non-adhering cells.

In the following, we respond to each question of the reviewers in detail.

1) The multiple scales at which different properties are defined makes it still difficult to figure the model out. Definition of the cell-to-cell contact energy J_{c,c} (subsection “2.1 Model setup”) and of averages in Figure 3 would help. The transition between the site and cell scale seems to be problematic if more than one cell have the same identifying string, which would happen if mutations do not happen (subsection “4.2 Evolutionary dynamics”), or if connectedness within cells is not ensured (subsection “4.1 Cell dynamics”).

We now make clear that J_cc is the energy between two adjacent lattice sites belonging to different cells (subsection “2.1 Model setup”), and that average J is obtained by averaging the J values extracted from the simulation, after steady state is reached (see caption of Figure 5).

The ligand and receptor strings are not used as an identifier for cells, therefore two cells can have the same bitstrings (e.g. as an outcome of cell division without mutations). The identifier of a cell ‘c’ is the number ‘s’ (a positive integer) which is also used as the spin value for the lattice sites composing the cell. It is by definition different between cells. With N cells there are N+1 different spin values (N cells plus the medium, which is given spin=0). In practice, this means that for each cell, the unique identifier (and the spin value of its composing lattice sites) is a positive integer.

After cell division, the lattice sites composing two cells are given different spin values. Moreover, in the Hamiltonian, the function for adhesion energy includes a (1 – Kronecker delta) term, which calculates adhesion energy only between lattice sites that have different spins (i.e. only at the cell surface).

Regarding connectivity: the chances that a cell loses connectivity with some of its lattice sites is rare, and when it happens it is quickly resolved because disconnected pixels have large contact with the medium (or with another cell) and thus large energy.

We now state that each cell is given a unique identifier corresponding to the spin value, at the beginning of the model presentation in the main text (subsection “2.1 Model setup”).

We clarify that the spin value of lattice sites is used as the identification number of cells in the Materials and methods section.

We now also state that a new spin value is assigned after cell division to ensure uniqueness of cell identity (subsection “Replication”).

2) We do not see how negative surface tension may imply 'repulsion' (subsection “Evolutionary model”, subsection “The evolution of uni- or multicellular strategies depends on environment stability”, Discussion section) between cells, rather than just an average higher probability for sites at the cell surface of sticking to the medium than to other cells. 'dispersion', also, may be due to amplification of fluctuations by persistence on the time scale of cell velocity update. Description of the behaviour of cells in isolation, especially how cell displacement depends on the magnitude of negative gamma would be very useful.

We agree with the reviewers that “repulsion” is a confusing term, and we substitute it with “dispersion” throughout the text.

As the reviewers suggest, negative surface tension implies that cells prefer contact with the medium over other cells, which leads to dispersal. Dispersal is faster for cells that do not adhere (gamma<0) than for cells that adhere neutrally (gamma=0), because cell–cell contact is energetically costly for negative gamma. The reason is that medium must penetrate in between two cells for them to detach, which is a random, energetically neutral process for gamma=0. For both gamma<0 and gamma=0 persistent cell migration speeds up dispersion. However, we see no difference in chemotaxis between the two cases (Figure 3A), indicating that the dispersal phase is very short. Once cells are sufficiently far apart, the gamma value no longer impacts their displacement because their behaviour depends only on J_{c,m}, which is constant in the chemotactic experiments (this is always the case for simulations run with only one cell).

We now mention in the main text that cells initially in a cluster disperse for gamma=0 and gamma<0 and once in isolation each cell behaves like those from simulations run with a single cell (i.e. as those in Figure 3“one cell”) (subsection “Strongly adhering cells perform efficient collective chemotaxis”).

We also include a video with an individual cell (Video 1) and one with a group of cells that do not adhere (Video 3).

We also refer to the video of a cluster of adhering cells in the same paragraph, in order to facilitate the comparison between these cases (subsection “Strongly adhering cells perform efficient collective chemotaxis”).

3) We do not understand the explanation for the evolution of small cell-to-cell adhesion for high frequency of environmental change. The authors claim that clusters always migrate faster up the gradient than single cells (Discussion section), but then in subsection “The evolution of uni- or multicellular strategies depends on environment stability” they seem to indicate the opposite. In the present formulation it is not clear if the advantage of single cells is given by the growing importance of the transient to clustering after reproduction and culling (that we imagine introduces, willing or not, a sort of local dispersal by creating 'holes' in coherent clusters), or by the fact that moving fast in one direction might not be the best strategy when such direction changes very fast (alike environmental response vs bet-hedging).

The single-cell strategy can be considered a type of bet-hedging: over multiple seasons cells spread out throughout the field (negative gamma ensures that cells do not cluster after division). By chance (and aided by inefficient chemotaxis), some cells will be located near the peak of the gradient at the end of every season. This strategy is at advantage when seasons change rapidly because a multicellular cluster does not have the time to reach the peak of the gradient and, over multiple seasons (and over multiple switching of the gradient direction) will end up at the centre of the field.

We now discuss this in the main text (subsection “The evolution of uni- or multicellular strategies depends on season duration”) and clarify the Discussion section and include two new videos (Video 6 and Video 7) that show the different behaviour of a cluster of adhering and non-adhering cells over a couple of short season (duration = 10000 times steps). In order to make the population dynamics clearer, we set mutation rate = 0 in both videos, and all cells are initialised with the same ligands and receptors (gamma = -4 in one video and gamma = 6 in the other).

These videos also address the reviewer’s remark regarding the evolutionary consequences of culling the population after cell division. Adhering cells indeed experience this transient local dispersal (far more than non-adhering ones, as the latter are already dispersed at the beginning of each season). As a consequence, adhering cells have a short period of about 2000 time steps in which collective migration is not as efficient as for a fully connected cluster.

We therefore hypothesise that in absence of such culling, the point at which multicellularity evolves shifts to slightly shorter seasons (about 2000 time steps shorter). The culling alone therefore does not explain the evolution of the unicellular strategy.

We now include this discussion in the main text (subsection “The evolution of uni- or multicellular strategies depends on season duration”).

4) We wonder if the fact that the string defining adhesion to the medium is a substring of that defining adhesion to other cells may have evolutionary effects, as the two kinds of adhesion will be in general correlated. We understand that this is a convenient choice, but are not sure that the existence of such correlations may be justified for cells. Secondly, by looking at Appendix Figure A2.1, we are puzzled by the statement that cell–cell variation in adhesion strength after evolution is small (subsection “The evolution of uni- or multicellular strategies depends on environment stability” and Appendix subsection “2.1 Adhesion strength distribution for τs = 100 x 103 MCS”), since no quantitative comparison is made with other situations (for instance, when the unicellular strategy evolves?).

We agree with the reviewers that using the same string for cell–cell and cell-medium adhesion could potentially have odd evolutionary consequences. We reasoned that if such correlations played an important role, the value of gamma from randomly generated ligands and receptors should differ from zero. We tested this by generating 100000 random pairs of ligands and receptors, and found that the average value of gamma is precisely zero, and the distribution of gamma values is symmetric around it.

The reason for this effective independence between cell–cell and cell-medium adhesion is that the number of possible configurations for each combination of the observed J values is very large, much larger than the number of combinations for which the overlap between cell–cell and cell-medium bits is important.

The same dataset of randomly generated ligands and receptors also provides a good “null model” for whether the variability of adhesion strength is narrowed by the evolutionary process. We observe that the variance of adhesion strength is smaller than that observed for random pairs of ligand-receptor strings (variance is respectively about 11 vs. 3.5).

We now mention this in Appendix 2.1 and Appendix—Figure A2.1 to include the data on random ligands and receptors. To facilitate the quantitative comparison between the two distributions we also mention the numerical values of the adhesion strength mean and variance for all the different cases shown in the figure (Appendix subsection “2.1 Adhesion strength distribution for τs = 100 x 103 MCS”).

https://doi.org/10.7554/eLife.56349.sa2

## Article and author information

### Author details

1. #### Enrico Sandro Colizzi

Mathematical Institute, Leiden University; Origins Center, Leiden, Netherlands
##### Contribution
Conceptualization, Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing
##### For correspondence
e.s.colizzi@math.leidenuniv.nl
##### Competing interests
No competing interests declared
2. #### Renske MA Vroomans

Informatics Institute, University of Amsterdam; Origins Center, Amsterdam, Netherlands
##### Contribution
Conceptualization, Software, Methodology, Writing - review and editing
##### Competing interests
No competing interests declared
3. #### Roeland MH Merks

Mathematical Institute, Leiden University; Institute of Biology, Leiden University; Origins Center, Leiden, Netherlands
##### Contribution
Supervision, Methodology, Writing - review and editing
##### Competing interests
No competing interests declared

### Funding

#### Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Nederlands Wetenschap Agenda StartImpuls)

• Enrico Sandro Colizzi
• Renske MA Vroomans

#### Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO/ENW-VICI 865.17.004)

• Roeland MH Merks

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

### Acknowledgements

We thank Paulien Hogeweg for constructive discussion. This research is supported by the Origins Center (NWA startimpuls). RM acknowledges his participation in the program ‘Cooperation and the Evolution of Multicellularity’ (2013) at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara that has inspired some of the ideas developed in this paper. All authors declare no conflict of interest.

### Senior Editor

1. Aleksandra M Walczak, école Normale Supérieure, France

### Reviewing Editor

1. Raymond E Goldstein, University of Cambridge, United Kingdom

### Publication history

2. Accepted: October 13, 2020
3. Accepted Manuscript published: October 16, 2020 (version 1)
4. Version of Record published: November 9, 2020 (version 2)

? 2020, Colizzi et al.

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# Spatially compartmentalized phase regulation of a Ca2+-cAMP-PKA oscillatory circuit

Brian Tenner et al.
Research Article

Signaling networks are spatiotemporally organized to sense diverse inputs, process information, and carry out specific cellular tasks. In β cells, Ca2+, cyclic adenosine monophosphate (cAMP), and Protein Kinase A (PKA) exist in an oscillatory circuit characterized by a high degree of feedback. Here, we describe a mode of regulation within this circuit involving a spatial dependence of the relative phase between cAMP, PKA, and Ca2+. We show that in mouse MIN6 β cells, nanodomain clustering of Ca2+-sensitive adenylyl cyclases (ACs) drives oscillations of local cAMP levels to be precisely in-phase with Ca2+ oscillations, whereas Ca2+-sensitive phosphodiesterases maintain out-of-phase oscillations outside of the nanodomain. Disruption of this precise phase relationship perturbs Ca2+ oscillations, suggesting the relative phase within an oscillatory circuit can encode specific functional information. This work unveils a novel mechanism of cAMP compartmentation utilized for localized tuning of an oscillatory circuit and has broad implications for the spatiotemporal regulation of signaling networks.

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2. Computational and Systems Biology