In biological organisms, networks of chemical reactions control the processing of information in a cell. A general approach to study the behavior of these networks is to analyze common modules. Instead of this analytical approach to study signaling networks, we construct functional motifs from the bottom up. We formulate conceptual networks of biochemical reactions that implement elementary algebraic operations over the domain and range of positive real numbers. We discuss how the steady state behavior relates to algebraic functions, and study the stability of the networks' fixed points. The primitive networks are then combined in feed-forward networks, allowing us to compute a diverse range of algebraic functions, such as polynomials. With this systematic approach, we explore the range of mathematical functions that can be constructed with these networks.

In distributed approaches to multiagent resource allocation, the agents belonging to a society negotiate deals in small groups at a local level, driven only by their own rational interests. We can then observe and study the effects such negotiation has at the societal level, for instance in terms of the economic efficiency of the emerging allocations. Such effects may be studied either using theoretical tools or by means of simulation. In this paper, we present a new simulation platform that can be used to compare the effects of different negotiation policies and we report on initial experiments aimed at gaining a deeper understanding of the dynamics of distributed multiagent resource allocation.