The severe acute respiratory syndrome (SARS) outbreak in 2002, which had a high morbidity rate and caused worldwide alarm, remains untreated today even though SARS was eventually isolated and controlled. Development and high-throughput screening of efficacious drugs is therefore critical. However, currently there remains a lack of such a safe system. Here, the generation and characterization of the first selectable, SARS-coronavirus (SARS-CoV)-based replicon cell line which can be used for screening is described. Partial SARS-CoV cDNAs and antibiotic resistance/reporter gene DNA were generated and assembled in vitro to produce the replicon transcription template, which was then transcribed in vitro to generate the replicon RNA. The latter was introduced into a mammalian cell line and the transfected cells were selected for by antibiotic application. For the antibiotic-resistant cell lines thus generated, the expression of reporter gene was ensured by continued monitoring using fluorescent microscopy and flow cytometry. The suitability of this replicon cell line in drug screening was demonstrated by testing the inhibitory effect of several existing drugs and the results demonstrate that the SARS-CoV replicon cell lines provide a safe tool for the identification of SARS-CoV replicase inhibitors. The replicon cell lines thus developed can be applied to high-throughput screening for anti-SARS drugs without the need to grow infectious SARS-CoV.

A key aspect in managing resources for customer sites is to predict and assess the load associated with a site in order to figure out how best to allocate resources for the site over time and to efficiently schedule tasks. The cost associated with the site and return on investment are also key parameters. This paper describes work we have done in developing tools for answering these critical questions. The tools use both analytical models and discrete event simulations to predict performance and analyze costs needed for handling a customer workload while satisfying the service level objectives. These tools provide capacity and load planning, performance simulation, and cost and financial analyses. Our tools have been used successfully by several major customers, and those experiences have shaped how the tools have evolved over time.

ReAgents are remotely executing agents that customize Web browsing for non-standard resource-limited clients. A reAgent is essentially a "one-shot" mobile agent that acts as an extension of a client, dynamically launched by the client to run on its behalf at a remote, more advantageous, location. ReAgents simplify the use of mobile agent technology by transparently handling data migration and runtime network communications, and provide a general interface for programmers to more easily implement their application-specific customizing logic. This is made possible by the availability of remote behaviors, i.e., common patterns of actions that exploit the ability to process and communicate remotely. Examples of such behaviors are transformers, monitors, cachers, and collators. In this paper, we identify a set of useful reAgent behaviors for interacting with Web services via a standard browser, describe how to program and use reAgents, and show that the overhead of using reAgents is low and outweighed by its benefits.

We present a performance analysis of an agent-based middleware system we have developed based on "reAgents," remotely executing agents that enhance the performance of client/server-based Internet applications. ReAgents simplify the use of mobile agent technology by transparently handling data migration and run-time network communications, and provide a general interface for programmers to more easily implement application-specific logic. This is made possible through the use of behaviors, i.e., common patterns of actions that exploit the ability to process and communicate remotely. We model the performance of each of the primary behaviors, and provide an experimental evaluation of reAgent performance.