SGER: Evolutionary Algorithms for Pathogen Defense

SGER：病原体防御的进化算法

• 批准号: 0233901
• 负责人: Lori Stevens
• 金额: $ 10万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0233901&HistoricalAwards=false
• 关键词: SGER; Evolutionary; Algorithms; Pathogen; Defense

Evolutionary computation uses principles from biological evolution to find optimal solutions to problems in engineering and computer science. Evolutionary algorithms are ideal for studying natural selection and optimization of the myriad of physiological processes that occur in organisms. In contrast, many other current modeling methods are limited in their ability to include dynamic, multi-objective optimization processes and results may be biased by initial parameter settings. Parameters in evolutionary algorithms can self-adapt, mimicking the evolution of processes such as mating system, migration, recombination and mutation. This research will use evolutionary algorithms to study multi-objective evolution of pathogen defense and local adaptation. An experiment using Tribolium flour beetles will validate the model and demonstrate the synergy between the computational efforts and empirical work. The project has broader impacts including stimulating an emerging research area through the novel application of recent developments in computer science and engineering to evolutionary physiology. Undergraduate, graduate and post-doctoral students will be trained in computational biology and the evolutionary physiology of host-pathogen interactions. The models will demonstrate the utility of evolutionary algorithms for modeling multiple defenses, self-adaptation, and spatial and temporal variation in pathogen prevalence. The topic of pathogen defense is a timely problem in both basic and applied biology, medicine and agriculture. These studies are relevant to genetically modified foods, and changes in pathogen abundance caused by global warming, as well as emerging infectious disease and drug resistance.

进化计算使用生物进化的原理来寻找工程和计算机科学问题的最佳解决方案。进化算法是研究自然选择和优化生物体中发生的无数生理过程的理想方法。 相比之下，许多其他当前建模方法在其包括动态多目标优化过程的能力方面受到限制，并且结果可能会受到初始参数设置的影响。 进化算法中的参数可以自适应，模仿交配系统、迁移、重组和变异等进化过程。 本研究将利用进化算法研究病原体防御和局部适应的多目标进化。一个使用拟谷盗的实验将验证该模型，并证明计算工作和经验工作之间的协同作用。 该项目具有更广泛的影响，包括通过将计算机科学和工程的最新发展应用于进化生理学来刺激一个新兴的研究领域。 本科生，研究生和博士后学生将接受计算生物学和宿主-病原体相互作用的进化生理学培训。 这些模型将展示进化算法在病原体流行的多重防御、自适应和时空变化建模中的实用性。病原体防御的话题是基础和应用生物学、医学和农业中的一个及时的问题。 这些研究与转基因食品、全球变暖引起的病原体丰度变化以及新出现的传染病和耐药性有关。