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Prebiotic Competition between Information Variants, With Low Error Catastrophe Risks

机译：信息变量之间的益生元竞争，具有低错误突变风险

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During competition for resources in primitive networks increased fitness of an information variant does not necessarily equate with successful elimination of its competitors. If variability is added fast to a system, speedy replacement of pre-existing and less-efficient forms of order is required as novel information variants arrive. Otherwise, the information capacity of the system fills up with information variants (an effect referred as “error catastrophe”). As the cost for managing the system’s exceeding complexity increases, the correlation between performance capabilities of information variants and their competitive success decreases, and evolution of such systems toward increased efficiency slows down. This impasse impedes the understanding of evolution in prebiotic networks. We used the simulation platform Biotic Abstract Dual Automata (BiADA) to analyze how information variants compete in a resource-limited space. We analyzed the effect of energy-related features (differences in autocatalytic efficiency, energy cost of order, energy availability, transformation rates and stability of order) on this competition. We discuss circumstances and controllers allowing primitive networks acquire novel information with minimal “error catastrophe” risks. We present a primitive mechanism for maximization of energy flux in dynamic networks. This work helps evaluate controllers of evolution in prebiotic networks and other systems where information variants compete.
机译：在原始网络中争夺资源的过程中，信息变体的适应性不一定等同于成功消除其竞争对手。如果将可变性快速添加到系统中，则随着新型信息变体的到来，需要快速替换先前存在的效率较低的订单。否则，系统的信息容量将充满各种信息变体（一种称为“错误灾难”的效应）。随着管理系统极其复杂的成本增加，信息变体的性能与其竞争成功之间的相关性会降低，并且此类系统朝着提高效率的方向发展会变慢。这种僵局阻碍了对益生元网络中进化的理解。我们使用模拟平台生物抽象双重自动机（BiADA）来分析信息变体如何在资源有限的空间中竞争。我们分析了与能源相关的功能（自动催化效率差异，订单能源成本，能源可用性，转化率和订单稳定性）的影响。我们讨论了允许原始网络以最小的“错误灾难”风险获取原始信息的情况和控制器。我们提出了一种在动态网络中最大化能量通量的原始机制。这项工作有助于评估益生元网络和信息变异竞争的其他系统中进化的控制器。

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