Biophysical genetics of collective feeding in C. elegans

点击翻译按钮获取中文摘要

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.