Games of multicellularity

Kaveh, Kamran; Veller, Carl; Nowak, Martin A.

doi:10.1016/j.jtbi.2016.04.037

Cited by 17 publications

(16 citation statements)

References 75 publications

(100 reference statements)

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“…An explicit connection between fragmentation modes and games played within the group has been first made by [Kaveh et al, 2016]. There, authors focused on fragmentation modes in a form x + 1, and explicitly considered the 2+1 life cycle.…”

Section: Discussionmentioning

confidence: 99%

“…Given that reproduction via single cell bottlenecks is a natural policing mechanisms, some aspects of the evolution of reproduction modes have been considered before. Examples are the evolution of propagule size [Roze and Michod, 2001, Michod and Roze, 2001], as well as the comparison between formation of cell clusters and unicellular existence [Kaveh et al, 2016]. However, the spectrum of possible interactions between cells goes way beyond specific scenarios of cooperation and the division of labour, so this topic remains largely unexplored.…”

Section: Introductionmentioning

confidence: 99%

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Interacting cells driving the evolution of multicellular life cycles

Gao

Traulsen

Pichugin

2019

Preprint

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7Evolution of complex multicellular life begun from the emergence of life cycle in-8 volving formation of cell clusters. Opportunity for cells to interact within clusters pro-9 vided them an advantage over unicellular life forms. However, what kind of interactions 10 may lead to the evolution of multicellular life cycles? Here, we combine evolutionary 11 game theory with a model for the emergence of multicellular groups to investigate how 12 cell interactions can influence reproduction modes during the early stages of the evo-13 lution of multicellularity. We identify evolutionary optimal life cycles as those which 14 maximize the population growth rate. Among all interactions captured by two-player 15 games, only eight life cycles were found to be evolutionarily optimal. Moreover, the 16 vast majority of games promotes either of two classes of life cycles: (i) splitting into 17 unicellular propagules or (ii) fragmentation into two offspring clusters of equal (or al-18 most equal) size. Our findings indicate that the three most important characteristics, 19 determining whether multicellular life cycles will evolve, are average performance of 20 homogeneous groups, heterogeneous groups, and solitary cells.21

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interacting cells driving the evolution of multicellular life cycles

Gao

Traulsen

Pichugin

2019

Preprint

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“…Cell-based models can be conceived as mechanistic representations of evolutionary games 30 where the replication, interactions and death of individual agents are explicitly simulated using a system updated by a series of discrete events 31 . These models derive the global dynamics of the population from local interactions rules described for every single individual 32 .…”

mentioning

confidence: 99%

Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`

Gregorio-Godoy

Rodríguez-Regueira

et al. 2018

Preprint

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Division of Labor can occur as a consequence of a major evolutionary transition such as multicellularity but is also found in societies of similar individuals like microbes. It has been defined as a process that occurs when cooperating individuals specialize to carry out specific tasks in a distributed manner. This paper analyzes the conditions for division of labor to emerge as a beneficial evolutionary solution and proposes two novel mechanisms for this process to emerge as a consequence of cell communication in an isogenic group of cells. The study is conducted by means of the cell-based model gro that simulates the growth and interaction of cells in a two-dimensional bacterial colony. When the labor is social, like the production of a molecule that is publicly shared, simulation results indicate that division of labor provides higher fitness than individual labor if the benefits of specialization are accelerating. Two genetic networks that 2 generate consensual and reversible specialization are presented and characterized. In the proposed mechanisms, cells self-organize through the exchange of certain molecules and coordinate behaviors at the local level without the requirements of any fitness benefits. In addition, the proposed regulatory mechanisms are able to create de novo patterns unprecedented to this date that can scale with size. MAIN TEXTMulticellularity is considered one of the major transitions in evolution, a concept that encompasses the phenomena in which natural selection transforms autonomously replicative genes/cells/individuals into new higher-level structures 1,2 . In such structures, individuals are no longer independent of each other but rather form a group that coordinates and communicates with a resulting behavior that is determined by the interaction of its fundamental elements 2 . One of this major transitions is multicellularity 3 . It has been identified that multicellularity has evolved independently more than 20 times, at least once in animals, three times in fungi, six times in algae, and multiple times in bacteria 4 .Multicellularity has involved different evolutionary pathways, leading to the belief that there is not a single explanation for its origins. However, some requirements have been identified across many species and are thought to be essential to meet the current definition of what constitutes a multicellular organism 5 . The evolutionary requirements for multicellularity include (i) the formation of clusters of cells via physical contact or adhesion to form a new evolutionary unit 6 , and (ii) communication among the individuals leading to coordinated activity 7 .There are some essential disadvantages to multicellularity. For example, it is much costly to adhere and communicate with your neighbors than to live a solitary life. Employing strategies that assure communication and encourage cooperation to reduce possible conflicts within the group does not 3 come for free 8 . Yet, these pitfalls can be greatly compensated by the benefits that multicellularity can provide. ...

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“…would be interesting to address the evolution of strategies through group selection, as in [19,25], together with the selection of group size.…”

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confidence: 99%

Hierarchical prisoner’s dilemma in hierarchical game for resource competition

2017

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Dilemmas in cooperation areone of the major concerns in game theory. In a publicgoods game, each individual cooperates by paying a cost or defectingwithout paying it, and receives a reward from the group out of the collected cost. Thus, defectingis beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individuals also play games. To study such a multi-level game, we introduce a hierarchical game in which multiple groups compete for limited resources by utilizing the collected cost in each group,where the power to appropriate resources increases with the population of the group. Analyzing this hierarchical game, we found a hierarchical prisoner's dilemma, in which groups choose the defecting policy (say, armament) as a Nash strategy to optimize each group's benefit, while cooperation optimizes the total benefit. On the other hand, for each individual, refusing to pay the cost (say, tax) is a Nash strategy, which turns out to be a cooperation policy for the group, thus leading to a hierarchical dilemma. Here the group reward increases with the group size. However,we find that there exists an optimal group size that maximizes the individual payoff. Furthermore, when the population asymmetry between two groups is large, the smaller group will choose a cooperation policy (say, disarmament) to avoid excessive response from the larger group, and the prisoner's dilemma between the groups is resolved. Accordingly, the relevance of this hierarchical game onpolicy selection in society and the optimal size of human or animal groups arediscussed.

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Games of multicellularity

Cited by 17 publications

References 75 publications

Interacting cells driving the evolution of multicellular life cycles

Interacting cells driving the evolution of multicellular life cycles

Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`

Hierarchical prisoner’s dilemma in hierarchical game for resource competition

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Games of multicellularity

Cited by 17 publications

References 75 publications

Interacting cells driving the evolution of multicellular life cycles

Interacting cells driving the evolution of multicellular life cycles

Deriving general conditions and mechanisms for division of labor using the cell-based simulator gro

Hierarchical prisoner’s dilemma in hierarchical game for resource competition

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Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`