OVERVIEW

MODULE 1.1

Overview of Computational Science

Scientific revolutions are "those non-cumulative developmental episodes in which in an older paradigm is replaced in whole or in part by an incompatible new one." —Thomas Kuhn, The Structure of Scientific Revolutions

Normally, "the scientific revolution" refers to the discoveries of the sixteenth and seventeenth centuries in Europe, which changed the western view of the natural world. This revolution began with the sun-centered universe of Copernicus and continued until Newton proposed universal gravitation and laws of motion. Nature was the object of much interest, and the exploration of the New World with all its discoveries continued to feed the desire to understand nature.

During the twentieth century, according to the eminent string-theory physicist Michio Kaku, there were three scientific revolutions—the quantum revolution, the biomolecular revolution, and the computer revolution (Kaku 1998). Few can doubt the rapidity with which recent scientific advances have been made with each new discovery or insight changing our view of our planet, its inhabitants, and often the universe. The accomplishments of that century augurs very well for the current one.

Early in the twenty-first century, Microsoft Research convened a workshop of international authorities to devise a "vision and roadmap of the evolution, challenges and potential of computer science and computing in scientific research during the next fifteen years." The outcome was "Towards 2020 Science." What they predicted marks the beginnings of a new scientific revolution, where computation will become more than an adjunct supporter of scientific research. Computational principles and tools will become integrated into science, changing the fundamental way that science is practiced. Computational science in both theoretical and experimental sciences will greatly augment the rates of scientific advances that will benefit the planet and our species (Microsoft Research 2006). For example, the results of the human genome project, which depended upon large-scale computational science, have encouraged a myriad of new research and development in government, university, and commercial laboratories. One significant outcome from these projects will be a far better understanding of molecular mechanisms that underlie human diseases and their more effective treatments.

In 2005, the President's Information Technology Advisory Committee released the report "Computational Science: Ensuring America's Competiveness" (Report to the President 2005). They concluded that computational science and high-performance computing could be integral to innovations in all of the sciences (biological/ biomedical, physical, and social), engineering, industry, and defense. Advances in computation allow us to acquire and analyze enormous streams of data, making it possible to consider and solve problems heretofore unapproachable. Computational science also allows us to build models, visualize phenomena, and conduct experiments difficult or impossible in the laboratory. We can now examine interactions in systems that involve more than one discipline, encouraging us to collaborate with specialists in other fields. Such collaboration should lead to solutions that are creative, synergistic, sustainable, and economically favorable.

Computational science, the fast-growing interdisciplinary field that is at the intersection of the sciences, computer science, and mathematics, will require scientists who are appropriately trained. The experts who produced "Towards 2020 Science" predicted that future scientists who are not computationally and mathematically literate will be unable to do science. Chemistry professor Robert Harrison, director of the Joint Institute for Computational Sciences at the University of Tennessee, states in the JICS Mission webpage, "To translate even the most elementary theories into useful tools for physical chemistry discovery, you have to do large-scale computation." He states further, "If you look at students coming into our graduate program from the undergraduate world, those that haven't already had some exposure to computation, such as thinking algorithmically, solving problems on the computer, and the little bits of applied math that you need to understand all of that, ... have lost a year or two of productivity at the graduate level. But it's not only the undergraduate students coming into graduate school that have this issue; it's also our undergrads going off into the larger world. Industry and many other aspects of the commercial world use simulation and computation in diverse ways" (JICS).

Computational science, which combines computer simulation, scientific visualization, mathematical modeling, computer programming, data structures, networking, database design, symbolic computation, and high-performance computing, can transform practices in a diverse range of disciplines. Its computer models and simulations offer valuable approaches to problems in many areas, as the following examples indicate.

1. Scientists at Los Alamos National Laboratory and the University of Minnesota wrote, "Mathematical modeling has impacted our understanding of HIV pathogenesis. Before modeling was brought to bear in a serious manner, AIDS was thought to be a slow disease in which treatment could be delayed until symptoms appeared, and patients were not monitored very aggressively. In the large, multicenter AIDS cohort studies aimed at monitoring the natural history of the disease, blood typically was drawn every six months. There was a poor understanding of the biological processes that were responsible for the observed levels of virus in the blood and the rapidity at which the virus became drug resistant. Modeling, coupled with advances in technology, has changed all of this." Dynamic modeling has not only revealed important features of HIV pathogenesis but has advanced the drug treatment regime for AIDS patients (Perelson and Nelson 1999). Since then, Perelson and other researchers have applied modeling to enhance our understanding of the hepatitis C virus, which causes widespread infections and is the primary cause of liver cancer in the United States. Such models have already revealed much about the pathogenesis of the virus, the effectiveness of treatments (interferon/ribavirin and direct antiviral agents), and the influence of genetic variants in the kinetics of the virus (Dahari et al. 2011).

2. From the 1960s, numerical weather prediction has revolutionized forecasting. "Since then, forecasting has improved side by side with the evolution of computing...