Machine Learning and Individual-Based Simulation for Theoretical Biology

理论生物学的机器学习和基于个体的模拟

• 批准号: RGPIN-2014-06007
• 负责人: Gras, Robin
• 金额: $ 1.46万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567527
• 关键词: Machine; Learning; Individual; Based; Simulation

We study the evolutionary process and the emergence of species in a simulated ecosystem. We have conceived EcoSim, an individual-based evolving predator-prey ecosystem simulation. The behavioral model of each individual is unique and is the outcome of the evolution process. One major and unique contribution of this tool is that it combines a behavioral, an evolutionary and a speciation mechanism. We have shown that we can fruitfully apply machine learning techniques to extract the most significant features and provide an easily understandable model to explain the main characteristics of the problem. We will add several functionalities to the simulation to allow more realistic ecological niches to emerge through the evolution of features such as the size, the strength or the perception capacities. Then, we will apply our approach to investigate two new difficult open questions using machine learning approaches to provide semantically rich models and rules that be used by biologists. Choosing an individual as a mate is governed by sexual selection. The females have special preferences for males according to their traits. What still remains controversial is the underlying genetic mechanisms that allows the evolution of female preferences. It is well known that there exist two main genetic quality benefits: good genes and compatible genes. However, the respective role of each one of them in the resulting observed genetic diversity is still not clearly understood. We will investigate multiple hypotheses using several mate choice methods. We will analyze number of different runs, varying many factors and environmental conditions, to understand in which conditions and at what cost such kind of mating strategy could be favored by evolution. Although asexual reproduction is relatively rare in eukaryotes, it has several strong advantages over sexual reproduction. In light of these advantages, it is puzzling why sexual reproduction is so prevalent. We are interested in studying how and why sexual reproduction emerged. The goal of these simulations is to determine whether there is a relationship between level of resources and environmental stressors (predators or pathogens) and mode of reproduction. We will be able to test many hypotheses in different configurations and evaluate what are the circumstances favorable to the apparition and diffusion of sexual reproduction.

我们研究了一个模拟生态系统的进化过程和物种的出现。我们构思了EcoSim，一个基于个体进化的捕食者-猎物生态系统模拟。每个个体的行为模式都是独一无二的，是进化过程的结果。这个工具的一个主要和独特的贡献是它结合了行为、进化和物种形成机制。我们已经证明，我们可以有效地应用机器学习技术来提取最重要的特征，并提供一个易于理解的模型来解释问题的主要特征。我们将在模拟中添加一些功能，以允许通过诸如大小，强度或感知能力等特征的进化来出现更真实的生态位。然后，我们将应用我们的方法来研究两个新的困难的开放问题，使用机器学习方法来提供生物学家使用的语义丰富的模型和规则。选择一个个体作为配偶是由性选择决定的。雌性根据自己的特点对雄性有特殊的偏好。仍然存在争议的是允许女性偏好进化的潜在遗传机制。众所周知，遗传质量效益主要有两种：优良基因和相容基因。然而，它们各自在由此观察到的遗传多样性中的作用仍然不清楚。我们将使用几种配偶选择方法来研究多种假设。我们将分析不同的运行次数，改变许多因素和环境条件，以了解在哪些条件和代价下，这种交配策略可能会被进化所青睐。尽管无性生殖在真核生物中相对罕见，但它比有性生殖有几个强大的优势。考虑到这些优势，有性繁殖为何如此普遍令人费解。我们感兴趣的是研究有性生殖是如何以及为什么出现的。这些模拟的目的是确定资源水平和环境压力源（捕食者或病原体）和繁殖模式之间是否存在关系。我们将能够在不同的配置中测试许多假设，并评估有利于有性生殖的出现和扩散的环境。