Rewarding and aversive stimuli evoke very different patterns of behavior and are rapidly discriminated. Here taste stimuli of opposite hedonic valence evoked opposite patterns of dopamine and metabolic activity within milliseconds in the nucleus accumbens. This rapid encoding may serve to guide ongoing behavioral responses and promote plastic changes in underlying circuitry.

The normal and abnormal behavior of a living cell is governed by complex networks of interacting biomolecules. Models of these networks allow us to make predictions about cellular behavior under a variety of environmental cues. In this review, we focus on two broad classes of such models: biochemical network models and statistical inference models. In particular, we discuss a number of modeling approaches in the context of the assumptions that they entail, the types of data required for their inference, and the range of their applicability.