At a microscopic level, organisms are ruled by interacting systems
of biomolecules. Historically, scientists painstakingly elucidated
chains of molecular events using experiments that reveal individual
interactions, although they recognized that members of different
pathways frequently interact. In recent years, researchers have
built richer, interconnected networks to mathematically summarize
their knowledge of these interactions. This systems biology
enterprise, largely stimulated by high-throughput tools like
microarrays that measure mRNA levels as an indicator of gene
expression, is a vital and increasingly important activity in both
basic biology and in medicine.

A nagging concern, however, is how accurately these networks
represent the biology. For complex systems like biological
networks, there are practical limits on how well even massive
amounts of data can uniquely define the underlying structure and
yield useful predictions of measurable events. Indeed, although its
advocates call this process "reverse engineering," the topology and
the detailed molecular interactions of the "inferred" networks will
likely never be known with precision.

This volume captures the ongoing process to assess the ability
of scientists--and their computer servants--to infer
networks from experimental data, by comparing their predictions to
"gold-standard" networks whose structure is thought to be
known.

NOTE: Annals volumes are available for sale as individual
books or as a journal. For information on institutional journal
subscriptions, please visit href="http://www.blackwellpublishing.com/nyas">www.blackwellpublishing.com/nyas.

ACADEMY MEMBERS: Please contact the New York Academy of
Sciences directly to place your order (href="http://www.nyas.org/">www.nyas.org).
Members of the New York Academy of Science receive full-text access
to the Annals online and discounts on print volumes. Please visit
href="http://www.nyas.org/MemberCenter/Join.aspx">http://www.nyas.org/MemberCenter/Join.aspx
for more information about becoming a member.