A molecular diffusion based utility model for Drosophila larval phototaxis

In this paper, light/dark choice-based Drosophila larval phototaxis is analyzed with our molecular diffusion based model. An ISCEM algorithm is developed to estimate the model parameters.By applying this behavioral utility model to light intensity and phototaxis data, we show that this model fits the experimental data very well.Our model provides new insights into decision making mechanisms in general. From an engineering viewpoint, we propose that the model could be applied to a wider range of decision making practices.Animals (including human beings) face the problem of choice making at both individual and population levels. Drosophila is a model animal that exhibits choice behaviors in various taxis responses. Decision making theories employ the concept of utility as a basis for choice. Utility maximization is a basic presumption of behavioral decisions [1]. If an animal consistently chooses one option in a given set of circumstances, that option is assigned a higher utility than the competing options at the time of decision. Insofar as choice is adaptive, the utilities of goal objects and activities can be considered subjective estimates of potential contributions to fitness [2]. Because utility plays an import role in decision making theories, it is meaningful to study the utility model from animal behaviors to help us make optimal judgments.Phototaxis is generally considered a form of light dependent preference behavior in animals. In the fruit fly Drosophila melanogaster, it is well known that wild type adults show positive phototaxis while negative phototaxis is seen in larvae [3-7]. From time of hatching to the early third instar stage, larvae robustly avoid light [3,8,9]. Immediately prior to pupation, light avoidance declines dramatically and animals become photoneutral, while adult flies are strongly attracted to light [3,8,9]. These behavioral changes undoubtedly reflect an innate search for suitable environments and a quest for survival. Early-instar l