Evolving Artificial Cell Signaling Networks using Molecular Classifier Systems

Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine cell signaling networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) artificial cell signaling networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of cell signaling networks focuses on four characteristic properties distinguished as follows: computation, evolution, crosstalk and robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named molecular classifier system (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's learning classifier system. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs