Modelling the evolution of biological complexity with a two-dimensional lattice self-assembly process

生物复杂性如何在进化中增加的问题是生物学的基本问题，通常与模块化组织的想法有关，模块化组织的定义有多种生物结构和功能的方式。利用二维格子自组装过程作为蛋白质四元结构自组装的抽象模型，我们可以用遗传算法研究结构复杂性的演化。在这个过程中，方形瓷砖根据其侧面的相互作用结合在一起，形成复杂的形状。相互作用的规范代表了基因，而最终组装的结构代表了表型。遗传算法使基因发生突变，并对表型应用适应度函数。这个模型已经被详细研究过，并复制了进化系统的许多已知特征，以及其他研究得很好的生物基因-表型图谱。这个博士项目的目的将是使用这种方法来研究结构和功能的复杂性如何在进化过程中增加。晶格自组装模型(也称为‘Polyomino模型’)的优点是它允许对结构复杂性进行严格的定量定义。通过研究不同的突变事件，如点突变和基因复制，并有可能引入更高水平的复杂性，如调控和表观遗传过程，该项目将旨在确定复杂性如何在进化中增加。最近的研究表明，环境条件的变化可能是导致这一点的关键，这将是使用我们的方法研究的中心原因之一。

The question how biological complexity increases in evolution is fundamental to biology, and is often linked to the idea of modular organisation, which has been defined in a variety of ways for biological structures and functions. Using a two-dimensional lattice self-assembly process, which serves as an abstract model of protein quaternary structure self-assembly, we can study the evolution of structural complexity using genetic algorithms. In this process square tiles bind together according to the interactions on their sides to form complex shapes. The specification of the interactions represents the genotype, and the final assembled structure represents the phenotype. The genetic algorithm mutates the genotype and applies a fitness function to the phenotype. This model has already been studied in detail and reproduces a number of known characteristics of evolving systems, and of other well-studied biological genotype-phenotype maps. The aim of this PhD project will be to use this approach to examine how structural and functional complexity can increase in the course of evolution. The advantage of the lattice self-assembly model (also called the 'Polyomino model') is that it allows for a rigorous quantitative definition of structural complexity. By examining different mutation events, such as point mutations and gene duplications, and with the possibility of introducing further levels of complexity, such as regulatory and epigenetic processes, this project will aim to establish how complexity can increase in evolution. Recent work has shown that changing environmental conditions may be key to this, and this will be one of the central causes examined using our approach.