This study focuses on methods for global organization of all known
protein sequences.  The main objective of this research is to explore
high order organization in the protein sequence space, and to identify
clusters of related proteins, by utilizing information about pairwise
similarities.  By these means we wish to achieve a classification of
all protein sequences into groups of shared biological function
(protein families), and gain a better understanding of the geometry of
the sequence space.
  
The category of this work is ``computational molecular biology''.
This is an essentially new discipline which is rooted in two different
disciplines: computer science and molecular biology.  Being on the
border line between the two disciplines, this study is related to
fields of intensive research in both.  It incorporates several recent
developments in the theory of metric embedding and unsupervised
learning, efficient graph algorithms, algorithms for the comparison of
protein sequences, and the statistical theory of sequence comparison.
While a full survey of each field is beyond the scope of this thesis,
I have provided sufficient background to evaluate this work.

This chapter begins with a very brief introduction to protein
sequences, then defines the basic problem on which this study is
focused, and introduces the methods that are employed.  An elaborated
introduction and discussion of each subject appears in the following
chapters.