This workshop is aimed at bringing together and creating synergies between researchers working on software agents and grid computing, on the one hand, and medicine,
computational biology and bioinformatics, on the other hand, to discuss relevant issues and approaches aimed at assessing and promoting the adoption of
innovative technologies in such fields.
There is barely a country that is not suffering from the ever increasing impacts and costs for its health care system which is not the least boosted by the steadily increasing number of drugs, diagnosis tools and methods, and treatment procedures that appear continuously on the market. On the other hand, in today’s globalized world, a fast and reliable medical prevention, diagnosis and treatment is of eminent importance as can be seen, for example, from the recent problems with SARS or the bird flu. Such highly contagious and lethal diseases can threaten the globe if they were not fought immediately with the highest level of efficiency and reliability. This requires, first of all, a fast and reliable pre-detection and diagnosis, regardless of where the affected person may currently stay in the world. The situations mentioned above are some of the many that prove that the medical domain is marked by requirements for high dynamicity, distribution, flexibility, scalability, extensibility, efficiency, cooperative work and collaboration, proactivity and autonomy. Most of these features are especially strongholds of multi-agent systems/autonomous agent technology. Thus, it is proper to say that agent technology will play an increasingly important role in medicine and health care in now and the future, and will significantly enhance the ability to model, design, and build complex, inherently distributed, software systems in medical and health care domains.
As for bioinformatics and computational biology, they are emerging disciplines that use information technology to organize, analyze, and distribute
biological information in order to answer complex biological questions. In particular, bioinformatics typically refers to activities that involve
researching, developing, or applying computational tools and techniques aimed at dealing with biological data –including those to acquire, store,
organize, archive, analyse, or visualize them. On the other hand, Computational Biology refers to the development and use of analytical data and
theoretical methods, mathematical modelling and simulation techniques aimed at studying biological, behavioural, and social systems. The amount of
available information is constantly increasing, and it is difficult to exploit available data from all sources. Many of the available data are
interrelated, but it is currently difficult to identify, select, clean, or use all relevant data, as different tools use different data formats
with different semantics. There is a need to devise methods aimed at learning and discovering knowledge by “intelligently” combining these distributed
data and information sources. In particular, after experiments are run, interpreting results requires gathering together potentially related data.
Also, the context in which an experiment is run, such as the hypothesis to be tested or the legal constraints of the institution, may inform which
resources are appropriately combined, again requiring “intelligence”. Moreover, some classical problems could be better tackled by resorting to a
suitable computational paradigm using various interaction protocols, e.g., cooperation or competition.
Furthermore, recent progresses in the application of bioinformatics and computational biology to biomedicine has made it essential to devise technological platforms able to ensure appropriate support to research activities performed in the area of life sciences. The increasing amount, complexity, and heterogeneity of biological data, together with the increasing production of the corresponding scientific literature, raised novel challenges. To support the most relevant tasks, a new generation of infrastructures, systems, and data mining algorithms still needs to be developed –able to perform, in particular, human genome analysis, protein interactions analysis, and molecular dynamics simulations. As for low-level infrastructures, let us stress the importance of GRID computing. In fact, the potential of GRID technology will be useful to deal with the complexity of models and with the enormous amount of available data (e.g. when searching the human genome or when carrying out simulations of molecular dynamics for the study of new drugs).
In principle, the workshop will mainly focus on the benefits of adopting agent and grid-based technologies in: (i) storing, accessing, and distributing
relevant medical or biological data, (ii) implementing the automation of information-gathering and information-inference processes in medical and
biological settings, (iii) supporting e-Health care, (iv) simulating and modelling biological systems.
Many applications, strictly or loosely related with medicine, computational biology, and bioinformatics, would benefit from agent technology and/or
To give the reader a flavour of the relevant classes of applications, let us recall:
Medicine and Health care:
Computational Biology and Bioinformatics: