Effects of airway surface liquid height on the kinetics of extracellular nucleotides in airway epithelia.

Experimental techniques aimed at measuring the concentration of signaling molecules in the airway surface liquid (ASL) often require an unrealistically large ASL volume to facilitate sampling. This experimental limitation, prompted by the difficulty of pipetting liquid from a very shallow layer (~15 µm), leads to dilution and the under-prediction of physiologic concentrations of signaling molecules that are vital to the regulation of mucociliary clearance. Here, we use a computational model to describe the effect of liquid height on the kinetics of extracellular nucleotides in the airway surface liquid coating respiratory epithelia. The model consists of a reaction-diffusion equation with boundary conditions that represent the enzymatic reactions occurring on the epithelial surface. The simulations reproduce successfully the kinetics of extracellular ATP following hypotonic challenge for ASL volumes ranging from 25 µl to 500 µl in a 12-mm diameter cell culture. The model reveals that [ATP] and [ADO] reach 1200 nM and 2200 nM at the epithelial surface, respectively, while their volumetric averages remain less than 200 nM at all times in experiments with a large ASL volume (500 µl). These findings imply that activation of P2Y2 and A2B receptors is robust after hypotonic challenge, in contrast to what could be concluded based on experimental measurements of volumetric concentrations in large ASL volumes. Finally, given the central role that ATP and ADO play in regulating mucociliary clearance, we investigated which enzymes, when inhibited, provide the greatest increase in ATP and ADO concentrations. Our findings suggest that inhibition of NTPDase1/highTNAP would cause the greatest increase in [ATP] after hypotonic challenge, while inhibition of the transporter CNT3 would provide the greatest increase in [ADO].

Positive interactions may decrease cooperation in social dilemma experiments.

A social dilemma appears in the public goods problem, where the individual has to decide whether to contribute to a common resource. The total contributions to the common pool are increased by a synergy factor and evenly split among the members. The ideal outcome occurs if everyone contributes the maximum amount. However, regardless of what the others do, each individual is better off by contributing nothing. Yet, cooperation is largely observed in human society. Many mechanisms have been shown to promote cooperation in humans, alleviating, or even resolving, the social dilemma. One class of mechanisms that is under-explored is the spillover of experiences obtained from different environments. There is some evidence that positive experiences promote cooperative behaviour. Here, we address the question of how experiencing positive cooperative interactions - obtained in an environment where cooperation yields high returns - affects the level of cooperation in social dilemma interactions. In a laboratory experiment, participants played repeated public goods games (PGGs) with rounds alternating between positive interactions and social dilemma interactions. We show that, instead of promoting pro-social behaviour, the presence of positive interactions lowered the level of cooperation in the social dilemma interactions. Our analysis suggests that the high return obtained in the positive interactions sets a reference point that accentuates participants' perceptions that contributing in social dilemma interactions is a bad investment.

Role-separating ordering in social dilemmas controlled by topological frustration.

''Three is a crowd" is an old proverb that applies as much to social interactions as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anticoordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

Evolutionary mixed games in structured populations: Cooperation and the benefits of heterogeneity.

Evolutionary games on networks traditionally involve the same game at each interaction. Here we depart from this assumption by considering mixed games, where the game played at each interaction is drawn uniformly at random from a set of two different games. While in well-mixed populations the random mixture of the two games is always equivalent to the average single game, in structured populations this is not always the case. We show that the outcome is, in fact, strongly dependent on the distance of separation of the two games in the parameter space. Effectively, this distance introduces payoff heterogeneity, and the average game is returned only if the heterogeneity is small. For higher levels of heterogeneity the distance to the average game grows, which often involves the promotion of cooperation. The presented results support preceding research that highlights the favorable role of heterogeneity regardless of its origin, and they also emphasize the importance of the population structure in amplifying facilitators of cooperation.

Stochastic win-stay-lose-shift strategy with dynamic aspirations in evolutionary social dilemmas.

In times of plenty expectations rise, just as in times of crisis they fall. This can be mathematically described as a win-stay-lose-shift strategy with dynamic aspiration levels, where individuals aspire to be as wealthy as their average neighbor. Here we investigate this model in the realm of evolutionary social dilemmas on the square lattice and scale-free networks. By using the master equation and Monte Carlo simulations, we find that cooperators coexist with defectors in the whole phase diagram, even at high temptations to defect. We study the microscopic mechanism that is responsible for the striking persistence of cooperative behavior and find that cooperation spreads through second-order neighbors, rather than by means of network reciprocity that dominates in imitation-based models. For the square lattice the master equation can be solved analytically in the large temperature limit of the Fermi function, while for other cases the resulting differential equations must be solved numerically. Either way, we find good qualitative agreement with the Monte Carlo simulation results. Our analysis also reveals that the evolutionary outcomes are to a large degree independent of the network topology, including the number of neighbors that are considered for payoff determination on lattices, which further corroborates the local character of the microscopic dynamics. Unlike large-scale spatial patterns that typically emerge due to network reciprocity, here local checkerboard-like patterns remain virtually unaffected by differences in the macroscopic properties of the interaction network.

Distinguishing the opponents promotes cooperation in well-mixed populations.

Cooperation has been widely studied when an individual strategy is adopted against all coplayers. In this context, some extra mechanisms, such as punishment, reward, memory, and network reciprocity must be introduced in order to keep cooperators alive. Here, we adopt a different point of view. We study the adoption of different strategies against different opponents instead of adoption of the same strategy against all of them. In the context of the prisoner dilemma, we consider an evolutionary process in which strategies that provide more benefits are imitated and the players replace the strategy used in one of the interactions furnishing the worst payoff. Individuals are set in a well-mixed population, so that network reciprocity effect is excluded and both synchronous and asynchronous updates are analyzed. As a consequence of the replacement rule, we show that mutual cooperation is never destroyed and the initial fraction of mutual cooperation is a lower bound for the level of cooperation. We show by simulation and mean-field analysis that (i) cooperation dominates for synchronous update and (ii) only the initial mutual cooperation is maintained for asynchronous update. As a side effect of the replacement rule, an "implicit punishment" mechanism comes up in a way that exploitations are always neutralized providing evolutionary stability for cooperation.

On the scaling of mammalian long bones.

Although there is much data available on mammalian long-bone allometry, a theory explaining these data is still lacking. We show that bending and axial compression are the relevant loading modes and elucidate why the elastic similarity model failed to explain the experimental data. Our analysis provides scaling relations connecting bone diameter and length to the axial and transverse components of the force, in good agreement with experimental data. The model also accounts for other important features of long-bone allometry.