Multi-science decision support for HIV drug resistance treatment.
The complete cascade from genome, proteome, metabolome, and physiome, to health forms multiscale, multiscience systems and crosses many orders of magnitude in temporal and spatial scales. The interactions between these systems create exquisite multitiered networks, with each component in nonlinear contact with many interaction partners. Understanding, quantifying, and handling this complexity is one of the biggest scientific challenges of our time. In this paper we argue that computer science in general, and Grid computing in particular, provide the language needed to study and understand these systems, and discuss a case study in decision support for HIV drug resistance treatment within the European ViroLab project.
Sloot
Peter
P
Section Computational Science, University of Amsterdam, Amsterdam, 1098SJ, The Netherlands. sloot@science.uva.nl
Coveney
Peter
P
Bubak
Marian T
MT
Vandamme
Anne-Mieke
AM
O Nualláin
Breanndán
B
van de Vijver
David
D
Boucher
Charles
C
D019380:Anti-HIV Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D003195:Computer Communication Networks / Q000458:organization & administration*; D003199:Computer Systems; D003299:Cooperative Behavior; D020000:Decision Support Systems, Clinical; D024921:Drug Resistance, Multiple, Viral; D005060:Europe; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D008492:Medical Informatics Computing; D012984:Software
D019380:Anti-HIV Agents