CAREER: Digital Evolution and Biocomplexity - From Biological Theory to Computational Applications

自然进化具有惊人的能力，可以为生命系统所面临的挑战提供优雅的解决方案。 随着这些挑战变得越来越复杂，应对这些挑战的生物体也必须如此。 进化显然是一种强大的力量，但很难收集足够的数据来理解复杂的特征是如何产生并融入工作整体的。 该项目的双重目标是了解进化如何设计复杂的功能，并将这些知识应用于解决复杂的计算问题。 为了实现这一目标，研究人员将使用“数字生物体”作为模型系统。 这些是自我复制的计算机程序，受到突变和选择的影响，在自然进化过程中从头进化出复杂的特征。 研究人员将使用该系统进行研究，并将其作为生物教室和博物馆信息亭的教学平台的基础。数字生物的种群是易于处理和透明的，允许研究人员研究进化设计过程的各个方面。 例如，一个进化个体的谱系可以很容易地被追踪，并且沿着每个突变的贡献可以被量化。 研究人员将使用这些技术来研究几个基本问题：适应性进化的结果对小的随机事件有多大的偶然性？ 当种群到达进化的死胡同时，是由于多维基因型空间中的一个单一的、严重的“错误转向”，还是这是一个渐进的过程？ 有害突变对促进适应性进化有多重要？ 中性突变更重要吗？ 如何利用这些力来解决计算设计问题？ 最后，有害人群（例如，病原体或进化的计算机病毒）被迫进入进化的死胡同？

Natural evolution has an amazing ability to produce elegant solutions to the challenges faced by living systems. As these challenges become more complex, so too must the organisms that respond to them. Evolution is clearly a powerful force, yet it is difficult to gather sufficient data to understand how complex traits arise and are incorporated into the working whole. The twin goals for this project are to understand how evolution designs complex functions, and to apply this knowledge to solving complicated computational problems. To accomplish this, the investigators will use "digital organisms" as a model system. These are self-replicating computer programs, subject to mutations and selection, that evolve complex features de novo in a natural evolutionary process. The investigators will use this system both for research purposes and as the basis of a teaching platform in biology classrooms and museum kiosks. Populations of digital organisms are tractable and transparent, allowing researchers to study all aspects of the evolutionary design process. For example, the lineage of an evolved individual can easily be traced and the contributions of each mutation along the way can be quantified. The investigators will use these techniques to study several fundamental questions: How contingent are the outcomes of adaptive evolution on small random events? When populations reach evolutionary dead-ends, is it due to a single, severe 'wrong turn' in multidimensional genotypic space, or is this a more gradual process? How vital are deleterious mutations to promoting adaptive evolution? Are neutral mutations more important? How can these forces be harnessed to solve computational design problems? Finally, can harmful populations ( e.g., pathogens or evolving computer viruses) be forced into evolutionary dead-ends?