Agents in Bioinformatics

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions

Coordination, Cooperation, and Collaboration: Defining the C3 Framework

The term C3 refers to the framework of coordinative, cooperative and collaborative relationships within the realm of external supply chain partnerships. Each unique partnership offers both benefits and challenges within a supply chain and must be aligned with company and supply chain strategy in order to achieve maximum effectiveness. This paper aims to fill the current void in supply chain literature concerning C3 by defining each term based upon current supply chain research as well as give the most prevalent characteristics and differences between each “C” in this phase model. This research is then compared to the industry through a case study of a major international retailer. Finally, we propose a set of propositions that organizations can use to assess at what level their external relationships reside within the phase model as well as how companies move and evolve their relationships between the levels and what the trigger mechanisms are in this evolution

Social techniques for effective interactions in open cooperative systems

Distributed systems are becoming increasingly popular, both in academic and commercial communities, because of the functionality they offer for sharing resources among participants of these communities. As individual systems with different purposes and functionalities are developed, and as data of many different kinds are generated, the value to be gained from sharing services with others rather than just personal use, increases dramatically. This, however, is only achievable if participants of open systems cooperate with each other, to ensure the longevity of the system and the richness of available services, and to make decisions about the services they use to ensure that they are of sufficient levels of quality. Moreover, the properties of distributed systems such as openness, dynamism, heterogeneity and resource-bounded providers bring a number of challenges to designing computational entities that cooperate effectively and efficiently. In particular, computational entities must deal with the diversity of available services, the possible resource limitations for service provision, and with finding providers willing to cooperate even in the absence of economic gains. This requires a means not only to provide non-monetary incentives for service providers, but also to account for the level of quality of cooperations, in terms of the quality of provided and received services. In support of this, entities must be capable of selecting among alternative interaction partners, since each will offer distinct properties, which may change due to the dynamism of the environment. With this in mind, our goal is to develop mechanisms to allow effective cooperation between agents operating in systems that are open, dynamic, heterogeneous, and cooperative. Such mechanisms are needed in the context of cooperative applications with services that are free of charge, such as those in bioinformatics. To achieve this, we propose a framework for non-monetary cooperative interactions, which provides non-monetary incentives for service provision and a means to analyse cooperations; an evaluation method, for evaluating dynamic services; a provider selection mechanism, for decision-making over service requests; and a requester selection mechanism, for decision-making over service provision