Applied research is, by necessity, a distributed, collaborative process. To be useful, research methodologies must not only be applicable in such an environment, but must also be adaptive to the needs of human resources and specific research area requirements. This paper introduces eXtreme Researching (XR), an adaptation of eXtreme Programming (XP) by Ericsson, to support distributed telecommunications research and development. XR builds on XP and tailors it to meet the needs of applied industrial research. It adopts and extends the most useful elements of XP: collective ownership, planning game, continuous integration and metaphor and shows how they are applicable in multi‐site, research projects. XPWeb is developed as a tool to facilitate XR in a distributed research environment. XPWeb and XR are actively used by Ericsson Applied Research and have been shown to significantly increase output and efficiencies in multi disciplinary research projects. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract-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.

Abstract. The Broadcast Language is a programming formalism devised by Holland in 1975, which aims at improving the efficiency of Genetic Algorithms (GAs) during long-term evolution. The key mechanism of the Broadcast Language is to allow GAs to employ an adaptable problem representation. Fixed problem encoding is commonly used by GAs but may limit their performance in particular cases. This paper describes an implementation of the Broadcast Language and its application to modeling biochemical networks. Holland presented the Broadcast Language in his book "Adaptation in Natural and Artificial Systems" where only a description of the language was provided, without any implementation. Our primary motivation for this work was the fact that there is currently no published implementation of the Broadcast Language available. Secondly, no additional examination of the Broadcast Language and its applications can be found in the literature. Holland proposed that the Broadcast Language would be suitable for the modeling of biochemical models. However, he did not support this belief with any experimental work. In this paper, we propose an implementation of the Broadcast Language which is then applied to the modeling of a signal transduction network. We conclude the paper by proposing that with some refinements it will be possible to use the Broadcast Language to evolve biochemical networks in silico.

Abstract-We examine a potential role of signalling crosstalk in Artificial Cell Signalling Networks (ACSNs). In this research, we regard these ACSNs or Artificial Biochemical Networks (ABNs) as collectively autocatalytic (i.e., closed) reaction networks being able to both self-maintain and to carry out a distinct signal processing function. These signalling crosstalk phenomena occur naturally when different biochemical networks become mixed together where a given molecular species may contribute simultaneously to multiple ACSNs. It has been reported in the biological literature, that crosstalk may have effects that are both constructive (e.g., coordinating cellular activities, multi-tasking) and destructive (e.g., premature programmed cell death). In this paper we demonstrate how crosstalk may enable distinct closed ACSNs to cooperate with other. From a theoretical point of view, this work may give new insights for the understanding of crosstalk in natural biochemical networks. From a practical point view, this investigation may provide novel applications of crosstalk in engineered ABNs.

Abstract-In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the user's requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an efficient approach to termination of base GA search or a means to balance practical consideration such as quality of solution and the expense of computation. Our Quality time tradeoff operator (QTT) is user defined, and acts as a base level termination operator and also provides a fitness value for the meta-level GA. In this manner the amount of computation time spent on less encouraging configurations can be specified by the user. Our approach has been applied to a computationally intensive test problem which evaluates a large set of configuration settings for the base GAs. This approach should be applicable across a wide range of practical problems (e.g. routing, logistic and biomedical applications).