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• W1966324534 abstract "Evolutionary optimization has been successfully used to increase our understanding of key properties of biochemical systems. Traditional optimization is, however, often insufficient for gaining deeper insights into the evolution of such systems because usually there is a mutual relationship between the properties optimized by evolution and the properties of the environment. Thus, by evolving towards optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such ‘dynamic fitness landscapes’. We therefore argue that it is a promising approach to studying the evolution of biochemical systems. Indeed, recent studies have applied evolutionary game theory to key issues in the evolution of energy metabolism. Evolutionary optimization has been successfully used to increase our understanding of key properties of biochemical systems. Traditional optimization is, however, often insufficient for gaining deeper insights into the evolution of such systems because usually there is a mutual relationship between the properties optimized by evolution and the properties of the environment. Thus, by evolving towards optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such ‘dynamic fitness landscapes’. We therefore argue that it is a promising approach to studying the evolution of biochemical systems. Indeed, recent studies have applied evolutionary game theory to key issues in the evolution of energy metabolism. A dynamic fitness landscape depends on the properties of the population; therefore, the fitness landscape changes as the population evolves. A dynamic fitness landscape can result from interactions between population and environment and from the presence of coevolving competitors in the environment [8]. (The term ‘fitness landscape’ refers to a function describing the relationship between the fitness of an organism and its properties (i.e. strategy) in the typically multidimensional space of possible properties.) A strategy that, if adopted by a population, cannot be invaded by any other strategy. An evolutionarily stable strategy is always an optimal strategy in its environment. A mathematical framework for modeling evolution in dynamic fitness landscapes [8]. Game theory was originally developed to study the optimal behavior of agents in games such as the prisoner's dilemma. A fixed fitness landscape does not depend on the properties of the evolving populations, which implies that the environment (including the strategies of competitors) does not change with the evolving population. In a fixed fitness landscape, the population evolves towards properties that maximize its fitness [8]. (The term ‘fitness landscape’ refers to a function describing the relationship between the fitness of an organism and its properties (i.e. strategy) in the typically multidimensional space of possible properties.) An approach for analyzing pathway fluxes on the basis of stoichiometric restrictions and maximization of product yields [39]. A strategy that maximizes fitness in a fixed fitness landscape. Reaction rates in a metabolic pathway at steady state. A mathematical model for describing the dynamics of population growth. Population dynamical models enable the interplay between environment and population to be studied [7]. A situation in which two individuals simultaneously have the choice of cooperative or selfish behavior (defection). If both players cooperate they gain a high payoff (‘reward’), but if both players choose defection they gain a low payoff (‘penalty’). If one player cooperates and the other player defects, however, the cooperator gains the lowest payoff (‘suckers payoff’) and the defector gains the highest possible payoff (‘temptation’). Thus, it always pays for a player to defect, irrespective of what the other player is choosing. Rational players therefore end up defecting, although in principle they could gain a higher reward. The prisoner's dilemma is frequently used to study the evolution of altruistic behavior [15]. A game in which two players simultaneously have the choice between three strategies, namely ‘rock’, ‘scissors’ and ‘paper’. Each strategy beats one strategy but in turn is beaten by the third strategy: paper beats rock, rock beats scissors, and scissors beats paper. Thus, there is no optimal strategy in this game. A framework in evolutionary theory for studying the evolution of communication [49,50]. Mathematical procedures to find an optimum of an objective function. A framework in evolutionary game theory for explaining the evolution of inefficient use of common resources [52]. It is a generalized form of a multiplayer prisoner's dilemma." @default.
• W1966324534 created "2016-06-24" @default.
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• W1966324534 date "2005-01-01" @default.
• W1966324534 modified "2023-10-07" @default.
• W1966324534 title "Game-theoretical approaches to studying the evolution of biochemical systems" @default.
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• W1966324534 doi "https://doi.org/10.1016/j.tibs.2004.11.006" @default.
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