Biophysical genetics of collective feeding in C. elegans

行为遗传学的目标是了解行为的哪些方面是遗传的，哪些DNA序列差异是负责任的。长期以来，遗传学家一直不认同有基因“决定”特定行为的观点，并认识到现实要复杂得多。DNA序列的差异改变了蛋白质在细胞内的工作方式，这些分子水平上的变化影响了组成器官并最终影响到整个动物的细胞。一个给定的动物通常包含多个序列差异，这些序列差异可以相互作用，这进一步使预测复杂化。这就是为什么研究表现出令人感兴趣的行为的最简单的有机体往往是有用的。在这个提议中，我们试图理解集体行为的遗传学——也就是动物群体的行为。想想椋鸟群、蚁群和交通堵塞。这是一项艰巨的任务，但幸运的是，秀丽隐杆线虫，一种只有302个神经元的小动物，也能够进行一种简单的集体行为：一些秀丽隐杆线虫菌株在大群体中进食，而另一些则单独进食。在过去的15年里，遗传学家已经确定了几个基因，当它们发生突变时，这些基因会破坏蠕虫的集体摄食，但是为了理解这些基因影响行为的哪些方面，以及它们如何在一个完整的动物中协同工作，我们需要一个集体摄食的模型。对其他种类的集体行为的研究表明，遵循相对简单规则的动物，只取决于它们的邻居在做什么，可以产生复杂的群体行为。在这种方法中，我们写下一组规则，这些规则捕获了我们认为我们对系统的了解，然后在计算机上模拟结果。如果模拟动物的行为和真实动物一样，那么我们就有信心确定了正确的规则。建立良好模型的关键之一是要有良好的数据，所以第一步是记录蠕虫形成群体时的视频，并精确量化群体形成后它们是如何移动的。为了观察蠕虫是如何紧密聚集在一起运动的，我们将使用一种叫做荧光显微镜的技术，这种技术可以让我们看到蠕虫体内的特定部位，这使得它们更容易被识别。然后，我们将利用蠕虫如何移动的知识来设计一个跟踪算法，该算法可以自动识别组中的单个蠕虫。跟踪真实的动物将给我们提供与模拟结果相同的信息，使我们能够精确地比较模拟动物和真实动物。如果我们需要，我们将更新模拟中的规则，以实现模拟和实验之间更好的一致性，并了解蠕虫在过程中如何相互作用。通过重复实验循环和模拟突变蠕虫，我们也将开始了解哪些方面的行为是在遗传控制之下。尽管经过多年的研究，我们实际上仍然不知道为什么一些蠕虫品系会形成群体。集体喂养比单独喂养有什么优势吗？可能是这些计算能力非常有限的动物能够利用群体的智慧更有效地找到高质量的食物块。我们将使用我们的计算模型来预测有利于聚集或独居动物的食物斑块安排，然后用实验来测试这些预测。如果我们发现集体进食有利的环境，这可能会告诉我们为什么蠕虫会进化出集体行为。这项工作将促进我们对基因突变如何引起复杂行为变化的理解。它还将对线虫如何成群觅食提供一些见解，这可能对农业产生影响，因为一些植物寄生线虫可以形成群体，每年造成超过1000亿美元的作物损失。

The goal of behavioural genetics is to understand what aspects of behaviour are inherited and which DNA sequence differences are responsible. Geneticists have long dismissed the notion that there are genes 'for' particular behaviours and recognise that reality is more complicated. DNA sequence differences change how proteins work inside of cells and these changes at the molecular level influence the cells that make up organs and ultimately a whole animal. A given animal will typically contain multiple sequence differences that can interact with each other, further complicating prediction. That is why it is often useful to study the simplest organism that displays a behaviour of interest.In this proposal, we are trying to understand the genetics of collective behaviour--that is, the behaviour of groups of animals. Think starling flocks, ant colonies, and traffic jams. This is a daunting task, but fortunately, the nematode worm C. elegans, a small animal with only 302 neurons, is also capable of a simple kind of collective behaviour: some C. elegans strains feed in large groups while others feed alone. Over the last fifteen years, geneticists have identified several genes that disrupt worm collective feeding when they are mutated, but to understand which aspects of the behaviour these genes affect and how they work together in an intact animal, we need a model of collective feeding.Studies of other kinds of collective behaviour have shown that animals following relatively simple rules, which depend only on what their neighbours are doing, can give rise to complex group behaviours. In this approach, we write down a set of rules that capture what we think we know about the system and then simulate the results on a computer. If the simulated animals behave like the real animals, then we gain confidence that we have identified the right rules. One of the keys to good modelling is to have good data and so the first step is to record movies of worms as they form groups and to quantify exactly how they move once the groups have formed. To see how worms move in tight groups we will use a technique called fluorescence microscopy that allows us to see particular parts inside of worms, which makes them easier to identify. We will then use our knowledge of how worms move to design a tracking algorithm that can automatically identify individual worms in the group. Tracking real animals will give us the same kind of information that we will derive from the simulated results enabling us to precisely compare the simulated and real animals. If we need to, we will update the rules in the simulation to achieve better agreement between the simulations and experiments and learn how worms interact in the process. By repeating the cycle of experiment and simulation with mutant worms, we will also begin to understand which aspects of the behaviour are under genetic control.Despite years of study, we actually still do not know why some worm strains form groups. Is there some advantage to feeding collectively instead of alone? It could be that these animals with very limited computational capacity are able to take advantage of the wisdom of the crowds to find high quality food patches more efficiently. We will use our computational model to predict food patch arrangements that favour aggregating or solitary animals and then test those predictions experimentally. If we find environments where collective feeding is advantageous this might tell us why collective behaviour has evolved in worms.This work will advance our understanding of how sets of genetic mutations can give rise to changes in complex behaviours. It will also give some insight into how nematode worms forage as groups which may have implications in agriculture because some plant parasitic nematodes, which cause more than $100 billion in crop damages annually, can form swarms.