AF: Medium: Algorithmic Foundations for Phylogenetic Networks

系统发育，或进化史，在生物学中起着核心作用，作为理解所有生命多样性的框架。 在查尔斯·达尔文的《物种起源》中，物种进化史的描述采用了一棵树的形状。从那以后，树一直是最常用的结构来模拟进化历史。然而，尽管树木捕捉到了例如一个物种如何分裂成两个随后多样化的物种，以及遗传物质如何从祖先传递到后代，但它们未能捕捉到其他进化事件。例如，一些植物物种是由于其他物种之间的杂交而产生的。在微生物世界中，细菌通过各种方式水平传播遗传物质。在这些情况下，一棵树充其量只能给出一幅不完整的进化历史图，而在最坏的情况下，这幅图会非常误导人。事实上，在这些情况下，一个更合适的进化历史模型是系统发生网络，它通过纳入杂交和水平基因转移等进化事件来扩展树模型。尽管近年来在系统发育网络领域的研究活动有所增加，但其重建和评估在很大程度上仍然是临时过程，并且其对特定数据集的适用性有限。为了开发系统重建和评价系统发生网络的方法学，本研究课题的目的是开发（1）利用基因组数据评价系统发生网络质量的算法，（2）为自动推断进化历史而搜索系统发生网络空间的算法。拟议工作的结果将有助于显着扩大的适用性的生物群体的树是不合适的，以及了解系统发育网络模型，从而允许更系统地发展的方法，其准确的重建。此外，这些结果将能够更准确地重建生命之树，有助于揭示微生物基因组的多样化，促进植物和其他生物群体中杂交场景的重建，并有助于了解微生物对抗生素产生耐药性的机制。 该项目为跨学科研究领域的研究生和本科生提供了出色的培训机会。

Phylogenies, or evolutionary histories, play a central role in biology as a framework within which to understand all of Life's diversity. In Charles Darwin's Origin of Species, the depiction of an evolutionary history of species took the shape of a tree. Ever since, trees have been the most commonly used structure to model evolutionary histories. However, while trees capture how, for example, one species splits into two that subsequently diversify and how genetic material is transmitted from ancestor to descendants, they fail to capture other evolutionary events. For example, some plant species arise due to hybridization between pairs of other species. In the microbial world, bacteria transmit genetic material horizontally by various means. In these cases, a tree gives an incomplete picture of the evolutionary history at best, and a very misleading one in the worst case. Indeed, a more appropriate model of evolutionary histories in these cases is a phylogenetic network, which extends the tree model by incorporating evolutionary events such as hybridization and horizontal gene transfer. Despite an increased research activity in the area of phylogenetic networks in recent years, their reconstruction and evaluation remain largely ad hoc processes and limited in their applicability to specific datasets. To enable the development of methodologies for systematic reconstructing and evaluating phylogenetic networks, this project is aimed at developing (1) algorithms for evaluating the quality of phylogenetic networks using genomic data, and (2) algorithms for searching the phylogenetic network space to enable automatic inference of evolutionary histories. The outcome of the proposed work will help significantly extend the applicability of phylogeny to groups of organisms for which trees are inappropriate, as well as to understand the phylogenetic network model, thus allowing for a more systematic development of methodologies for its accurate reconstruction. Further, the results will enable a more accurate reconstruction of the Tree of Life, help unravel microbial genome diversification, facilitate the reconstruction of hybridization scenarios in plants and other groups of organisms, and help understand the mechanisms by which microbes develop resistance to antibiotics. The project provides outstanding opportunities for training graduate and undergraduate students in an interdisciplinary research area.