Cooperativity is a hallmark of numerous biological systems; Individual agents, be they proteins, cells or organisms organize into networks to coordinate their activity. This sharing of information allows the emergence of a functional whole with rich and reliable behavior that can interact with the environment over large spatial and temporal scales. Current understanding of the ingredients needed to establish a successful biological network is lacking.

We study one of the most magnificent manifestations of biological cooperation – the ant colony. Ensembles of insects can cooperate, through countless interactions, to form eusocial structures with diverse emergent behaviors that surpass the capabilities of any single member. These large-scale structures then feed back onto the microscopic scale and control the behavior of individuals. Social insects and ants in particular, provide a unique platform for developing the theoretical concepts needed to understand complex biological systems. This system stand out for permiting quantitative experimental observation and manipulation with little or no compromise to its functionality. This opens an important window onto understanding and quantifying its function.

Social insects may be examined at two different scales. At the smaller scale, individuals interact locally with other individuals and with the environment. They collect local, and thus fragmented, information that then undergoes a relatively simple processing by which an individual decides on her future actions. At the colony scale, a huge number of local interactions and actions exhibit robust and yet highly adaptive emergent behaviors responding to the global state of the colony and its environment. The social insect colony provides a unique experimental opportunity to track both individual and collective behavior simultaneously.

We are developing controlled experiments aimed at observation and manipulation on all relevant scales. These are employed to colonies as they perform collective actions such as dynamical shifts in task allocation.

When studying a complex biological system the analysis of data is at least as challenging as its collection: This system is composed of a large number of freely moving and stochastic components that respond to external stimulation by way of multitudes of interactions that are either local or via the manipulation of the environment. On the positive side, the biological function of the system provides paths by which analysis should be approached. Namely, the ant colony processes environmental and internal conditions as a way of optimizing food collection, nest maintenance and brood care towards maximizing the generation of offspring. Combining the detailed experimental data with tools from fields such as information theory and network theory we map out the phenomenological rules that govern collective behaviors in the colony. Our long term goal is to use this rich cooperative system to develop a theory of collective information processing.