Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies

Resistance to chemotherapies, particularly to anticancer treatments, is an increasing medical concern. Among the many mechanisms at work in cancers, one of the most important is the selection of tumor cells expressing resistance genes or phenotypes. Motivated by the theory of mutation-selection in adaptive evolution, we propose a model based on a continuous variable that represents the expression level of a resistance gene (or genes, yielding a phenotype) influencing in healthy and tumor cells birth/death rates, effects of chemotherapies (both cytotoxic and cytostatic) and mutations. We extend previous work by demonstrating how qualitatively different actions of chemotherapeutic and cytostatic treatments may induce different levels of resistance. The mathematical interest of our study is in the formalism of constrained Hamilton-Jacobi equations in the framework of viscosity solutions. We derive the long-term temporal dynamics of the fittest traits in the regime of small mutations. In the context of adaptive cancer management, we also analyse whether an optimal drug level is better than the maximal tolerated dose

The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity

<p>Abstract</p> <p>Background</p> <p>Recent work on the complexity of life highlights the roles played by evolutionary forces at different levels of individuality. One of the central puzzles in explaining transitions in individuality for entities ranging from complex cells, to multicellular organisms and societies, is how different autonomous units relinquish control over their functions to others in the group. In addition to the necessity of reducing conflict over effecting specialized tasks, differentiating groups must control the exploitation of the commons, or else be out-competed by more fit groups.</p> <p>Results</p> <p>We propose that two forms of conflict – access to resources within groups and representation in germ line – may be resolved in tandem through individual and group-level selective effects. Specifically, we employ an optimization model to show the conditions under which different within-group social behaviors (cooperators producing a public good or cheaters exploiting the public good) may be selected to disperse, thereby not affecting the commons and functioning as germ line. We find that partial or complete dispersal specialization of cheaters is a general outcome. The propensity for cheaters to disperse is highest with intermediate benefit:cost ratios of cooperative acts and with high relatedness. An examination of a range of real biological systems tends to support our theory, although additional study is required to provide robust tests.</p> <p>Conclusion</p> <p>We suggest that trait linkage between dispersal and cheating should be operative regardless of whether groups ever achieve higher levels of individuality, because individual selection will always tend to increase exploitation, and stronger group structure will tend to increase overall cooperation through kin selected benefits. Cheater specialization as dispersers offers simultaneous solutions to the evolution of cooperation in social groups and the origin of specialization of germ and soma in multicellular organisms.</p

Avoid, attack or do both? Behavioral and physiological adaptations in natural enemies faced with novel hosts

BACKGROUND: Confronted with well-defended, novel hosts, should an enemy invest in avoidance of these hosts (behavioral adaptation), neutralization of the defensive innovation (physiological adaptation) or both? Although simultaneous investment in both adaptations may first appear to be redundant, several empirical studies have suggested a reinforcement of physiological resistance to host defenses with additional avoidance behaviors. To explain this paradox, we develop a mathematical model describing the joint evolution of behavioral and physiological adaptations on the part of natural enemies to their host defenses. Our specific goals are (i) to derive the conditions that may favor the simultaneous investment in avoidance and physiological resistance and (ii) to study the factors that govern the relative investment in each adaptation mode. RESULTS: Our results show that (i) a simultaneous investment may be optimal if the fitness costs of the adaptive traits are accelerating and the probability of encountering defended hosts is low. When (i) holds, we find that (ii) the more that defended hosts are rare and/or spatially aggregated, the more behavioral adaptation is favored. CONCLUSION: Despite their interference, physiological resistance to host defensive innovations and avoidance of these same defenses are two strategies in which it may be optimal for an enemy to invest in simultaneously. The relative allocation to each strategy greatly depends on host spatial structure. We discuss the implications of our findings for the management of invasive plant species and the management of pest resistance to new crop protectants or varieties

Quorum sensing as a mechanism to harness the wisdom of the crowds

Bacteria release and sense small molecules called autoinducers in a process known as quorum sensing. The prevailing interpretation of quorum sensing is that by sensing autoinducer concentrations, bacteria estimate population density to regulate the expression of functions that are only beneficial when carried out by a sufficiently large number of cells. However, a major challenge to this interpretation is that the concentration of autoinducers strongly depends on the environment, often rendering autoinducer-based estimates of cell density unreliable. Here we propose an alternative interpretation of quorum sensing, where bacteria, by releasing and sensing autoinducers, harness social interactions to sense the environment as a collective. Using a computational model we show that this functionality can explain the evolution of quorum sensing and arises from individuals improving their estimation accuracy by pooling many imperfect estimates – analogous to the ‘wisdom of the crowds’ in decision theory. Importantly, our model reconciles the observed dependence of quorum sensing on both population density and the environment and explains why several quorum sensing systems regulate the production of private goods.</p

Author Sequence and Credit for Contributions in Multiauthored Publications

A transparent, simple, and straightforward approach that is free from any arbitrary rank valuation is required to estimate the credit associated with the sequence of authors' names on multiauthored papers

Peto's paradox and human cancers

Peto's paradox is the lack of the expected trend in cancer incidence as a function of body size and lifespan across species. The leading hypothesis to explain this pattern is natural selection for differential cancer prevention in larger, longer lived species. We evaluate whether a similar effect exists within species, specifically humans. We begin by reanalysing a recently published dataset to separate the effects of stem cell number and replication rate, and show that each has an independent effect on cancer risk. When considering the lifetime number of stem cell divisions in an extended dataset, and removing cases associated with other diseases or carcinogens, we find that lifetime cancer risk per tissue saturates at approximately 0.3-1.3% for the types considered. We further demonstrate that grouping by anatomical site explains most of the remaining variation. Our results indicate that cancer risk depends not only on the number of stem cell divisions but varies enormously (approx. 10 000 times) depending on anatomical site. We conclude that variation in risk of human cancer types is analogous to the paradoxical lack of variation in cancer incidence among animal species and may likewise be understood as a result of evolution by natural selection

The role for simulations in theory construction for the social sciences:Case studies concerning Divergent Modes of Religiosity

Religion is, at the very least, a highly complex social phenomenon. The theories we use to understand religion – and sociocultural systems more generally – are often so complex that even experts in the field may not be able to see all their consequences. Social simulations can help us understand and communicate the consequences of a theory, provided we can describe the theory with sufficient precision and comprehensiveness in order to run it on a computer. In this article we demonstrate the benefits of simulating the predictions of a well-known theory in the Cognitive Science of Religion, the theory of Divergent Modes of Religiosity. Many of these predictions have already been tested against contemporary and longitudinal evidence, using the methods of both qualitative case study and large-scale survey, and some of the mechanisms responsible for the patterns observed have been investigated by means of controlled experiments. Nevertheless, in simulating the patterns of religious transmission and transformation predicted by the modes theory we discovered numerous aspects that were underspecified, generating new hypotheses for investigation in future empirical research. This back-and-forth between simulation and theory testing has the potential to accelerate progress in the scientific study of religion

Experimental quantum speed-up in reinforcement learning agents

Increasing demand for algorithms that can learn quickly and efficiently has led to a surge of development within the field of artificial intelligence (AI). An important paradigm within AI is reinforcement learning (RL), where agents interact with environments by exchanging signals via a communication channel. Agents can learn by updating their behaviour based on obtained feedback. The crucial question for practical applications is how fast agents can learn to respond correctly. An essential figure of merit is therefore the learning time. While various works have made use of quantum mechanics to speed up the agent's decision-making process, a reduction in learning time has not been demonstrated yet. Here we present a RL experiment where the learning of an agent is boosted by utilizing a quantum communication channel with the environment. We further show that the combination with classical communication enables the evaluation of such an improvement, and additionally allows for optimal control of the learning progress. This novel scenario is therefore demonstrated by considering hybrid agents, that alternate between rounds of quantum and classical communication. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecom-wavelength photons and features a fast active feedback mechanism, allowing us to demonstrate the agent's systematic quantum advantage in a setup that could be readily integrated within future large-scale quantum communication networks.Comment: 10 pages, 4 figure