1) What are key grand challenges in our field-- both scientific and
technical?

A complete mechanistic understanding of function and structure of
living systems -- integrating bottom up (constructive) approaches
scaling up from ab-initio and molecular models, to top-down
(deconstructive) approaches that work down from funciton/phenotype
(disease) to underlying structures. While bottom-up techniques have
received significant research attention (with complex codes capable
of scalable parallel performance now available), relatively lesser
attention has been focused on a systems-level, deconstructive approach
to modeling. It is in this realm that significant potential for radical
advances appears likely.

Within systems level modeling, the key technical challenges include
high-throughput data generation, curation, and publication for large
number of species, effective and efficient tools for analysis,
inference, and modeling, statistical and computational tools for
bridging phenotype, genotype, and the associated interaction cascades,
and techniques for guiding experimental studies. The high-level
goal, of ocurse, is a comprehensive computational model of a living
cell, modeling all of the processes in complete detail, with the
ability to instrument change and observe behavior all the way from
atomistic detail to phenotype.


2) What is the impact of achieving them?



3) Which of these have computation as a bottleneck and what new theory or
algorithms need to be developed to achieve them?



4) What day-to-day science in our area could be aided by extreme computing-
not grand challenges but things we have to do anyway that are leading to
great science and important technological advance?