(687f) Detection and Control of Invading Species in Autocatalytic Networks

Spatially distributed systems hosting autocatalytic species are an excellent test bed as a complex network because of their non-linear structure and ability to display a rich spectrum of behavior. The network system is designed to produce a robust, autocatalytic host species. It so happens that an invasion in the form of another autocatalytic species with higher reproduction rates disrupts normal operation. When an invading species gains foothold in the system, the concentration of the host species decreases and thereby the network elicits different behavioral patterns. The complex network is supervised and tested under an agent based architectural framework which employs a hierarchical structure for agents distributed in different layers. The generic agent structure is comprised of a variety of agents designed for monitoring and control purposes. To combat infection, multi-agent strategies are employed to explore alternatives and intelligently control the network. This choice is ideal because agent based approaches are flexible, robust and can adapt to changing conditions.

In the study, a challenge is posed to investigate a 25 reactor grid network with unequal volumes attacked by a temporary disturbance of an invading cubic autocatalytic species in inflow. The analysis shows that after the disturbance is removed, the network displays interesting patterns. Depending on the magnitude of invasion, growth and death rates of the invading species, case studies demonstrate that undesirable species can disappear from the environment, coexist with the host, flush out the host species or propagate through the network as a disturbance. Elimination of the invader or disturbance rejection can be achieved by using alternative mechanisms, namely, flushing out the invading species, starving the invading species to death or adding an external entity or antibiotic to kill the undesired species.

Owing to the complex nature of the system, an agent based strategy provides a generic detection and control scheme for detection and control of invading species. With the invading species being an immeasurable quantity, there is a need to device a specialized invasion detector agent which controls based on system knowledge. As a knowledge building exercise, sans invasion, a large number of case studies were simulated using feed flow rate, interaction flow rate and resource concentration in the feed as free parameters. The data organize themselves into a manifold which relate the consumption of resource and the production of host autocatalytic species. A control ellipse is constructed using statistical tools to encapsulate normal operating data. For data points within the control ellipse, the gradient in concentration for host species and resource is often negative. A rule based strategy is used to build agents to raise alarms when invasion occurs. What happens is, invasion originates at a specific location in a network, the concentration gradients follow dynamic trajectory and finally settle down at a steady state. Rules are designed to capture invasion patterns at an early stage based on projecting the invasion data onto the control ellipse and estimating the changes in the gradients in concentration of resource and host species. Performance of the system was tested for different operating conditions. Depending on the degree of invasion in the system, agent based control is employed using a variety of manipulated variables.