Bioinformatics is the computing response to the molecular revolution in biology. This revolution has reshaped the life sciences and given us a deep understanding of DNA sequences, RNA synthesis, and the generation of proteins. In the process of achieving this revolution in understanding, we have accumulated vast amounts of data.The scale of this data, its structure, and the nature of the analytic task have merited serious attention from computer scientists and prompted work in intelligent systems, data mining, visualization, and more. It has also demanded serious efforts in large-scale data curation and developing a worldwide infrastructure to support this. Bioinformatics, the handmaiden of molecular biology, poses novel computational challenges, stretches the state of the art, and opens unanticipated uses of computing concepts. In tackling these, computer scientists have the additional satisfaction of contributing to a scientific Grand Challenge.Bioinformatics is, however, only the first step in reshaping the life sciences. For further progress, we must return to the study of whole biological systems: the heart, cardiovascular system, brain, and liver-systems biology. To build an integrated physiology of whole systems, we must combine data from the many rich areas of biological information. Alongside the genome, which constitutes our knowledge about genes, we place the proteome, metabolome, and physiome, which embody knowledge about proteins, metabolic processes, and physiology.Systems biology is at least as demanding as, and perhaps more demanding than, the genomic challenge that has fired international science and gained public attention. Progressing in this discipline will involve computer scientists working in close partnership with life scientists and mathematicians. In contrast to the molecular biology revolution, computer science will proactively engage in shaping the endeavor rather than just clearing up afterwards! The prize to be attained is immense. From in silico drug design and testing to individualized medicine that will take into account physiology and genetic profiles, systems biology has the potential to profoundly affect healthcare and medical science generally. THE ROLE OF MODELINGSuppose we had a catalog of all the gene sequences, how they translate to make proteins, and which proteins interact with each other. Further, assume Progress in the study of biological systems such as the heart, brain, and liver will require computer scientists to work closely with life scientists and mathematicians. Computer science will play a key role in shaping the new discipline of systems biology and addressing the significant computational challenges it poses.

Web Service orchestration engines need to be more open to enable the addition of new behaviours into service-based applications. In this paper, we illustrate how, in a BPEL engine with aspect-weaving capabilities, a process-driven application based on the Google Web Service can be dynamically adapted with new behaviours and hot-fixed to meet unforeseen postdeployment requirements. Business processes (the application skeletons) can be enriched with additional features such as debugging, execution monitoring, or an application-specific GUI.Dynamic aspects are also used on the processes themselves to tackle the problem of hot-fixes to long running processes. In this manner, composing a Web Service 'on-the-fly' means weaving its choreography interface into the business process.

More than 100,000 people have participated in controlled trials of statins (lowering cholesterol drugs) since the introduction of lovastatin in the 1980s. Meta-analyses of this data have shown that statins have a beneficial effect on treated groups compared to control groups, reducing cardiovascular risk. Inhibiting the HMG-CoA reductase in the liver, statins can reduce cholesterol levels, thus reducing LDL levels in circulation. Published data from intravascular ultrasound studies (IVUS) was used in this work to develop and validate a unique integrative system model; this consisted of analyzing control groups from two randomized controlled statins trials (24/97 subjects respectively), one treated group (40 subjects, simvastatin trial), and 27 male subjects (simvastatin, pharmacokinetic study). The model allows to simulate the pharmacokinetics of statins and its effect on the dynamics of lipoproteins (e.g., LDL) and the inflammatory pathway while simultaneously exploring the effect of flow-related variables (e.g., wall shear stress) on atherosclerosis progression.

Using a composite model of the glucose homeostasis system, consisting of seven interconnected submodels, we enumerate the possible behaviours of the model in response to variation of liver insulin sensitivity and dietary glucose variability. The model can reproduce published experimental manipulations of the glucose homeostasis system and clearly illustrates several important properties of glucose homeostasis-boundedness in model parameters of the region of efficient homeostasis, existence of an insulin sensitivity that allows effective homeostatic control and the importance of transient and oscillatory behaviour in characterizing homeostatic failure. Bifurcation analysis shows that the appearance of a stable limit cycle can be identified.