China
Africa
Explore chapters and articles related to this topic
Impact of Massively Parallel Computation on Protein Structure Determination
Published in Theo C. Pilkington, Bruce Loftis, Joe F. Thompson, Savio L-Y. Woo, Thomas C. Palmer, Thomas F. Budinger, High-Performance Computing in Biomedical Research, 2020
Richard C. Brower, Charles DeLisi
In the last few years, it has become increasingly obvious that massively parallel computers will drastically change the computational power devoted to a single problem. Structural biology, with its potential for both basic knowledge of biological systems and for biotechnology, ranks as one of the grand challenges of high-performance computing. However, after several decades of serial programming, it is not a trivial matter to exploit this power. Many approaches to parallel computing are possible. For example, if the goal is to explore a very large set of conformations of a single protein, the best and most obvious approach may be just to replicate the code across a large farm of processors and run the same systems thousands of times simultaneously. Very little interprocessor communication is required: only a reasonable network to monitor the multiple systems and to coordinate the resultant data. Such network parallelism has tremendous appeal for commercial applications since it builds on the existing infrastructure of individual workstations.
Artificial Enzymes
Published in Yubing Xie, The Nanobiotechnology Handbook, 2012
James A. Stapleton, Agustina Rodriguez-Granillo, Vikas Nanda
Enormous numbers of possible conformations are available to long polypeptide chains. The forces that govern folding are subtle, and the energetic difference between two competing folds can be minuscule. Predicting the three-dimensional structure that will be adopted by a given sequence is therefore extremely difficult and is one of the grand challenges of structural biology. Increasing computer power and clever algorithms have allowed researchers to make great progress in folding prediction in recent years, and the folds of most proteins of fewer than ~100 amino acids can now be predicted with reasonable accuracy (Zhang 2008). In considering artificial enzymes, we are perhaps more interested in the opposite problem: can we predict what sequences will adopt a given target three-dimensional structure? This is known as the protein design problem, and great progress has been made on this front as well. New sequences have been generated that adopt protein structures from nature (Dantas et al. 2003) as well as structures not yet found in nature (Kuhlman et al. 2003).
The uses of grand challenges in research policy and university management: something for everyone
Published in Journal of Responsible Innovation, 2022
The origin of the notion of grand challenges is in the United States, but it has been very influential also in the research policy of the European Union since the mid-1990s. There are significant differences in how such challenges are approached in these two contexts, having an impact on the uses of this notion in local research governance. One of the first uses of the term was in the High-Performance Computing Act (1991). The act listed ten grand challenges, including high-performance supercomputing, the prediction of severe weather events and air pollution (Hicks 2016). It defines a ‘Grand Challenge’ as ‘a fundamental problem in science or engineering, with broad economic and scientific impact, whose solution will require the application of high-performance computing resources’ (Gore 1991). Hence, grand challenges were primarily problems emerging from scientific interests with broad social applicability. The Bill & Melinda Gates Foundation endorsed the notion in the early 2000s, launching the ‘Grand Challenges for Global Health’ initiative to support research related to the UN’s Millennium Development Goals (Ulnicane 2016). In this usage, grand challenges were connected to global social issues and wellbeing.