\chapter{Conclusion}

In this paper we examined two programs, both designed to solve the
same types of problems in the same domain.  The first program used
compiled knowledge and empirical associations, and the second,
model-based reasoning.  The first program, {\sc Genex I}, performed
well, but because the domain knowledge was compiled into the rules,
the knowledge base was difficult to extend, and the system could not
access the knowledge to produce explanations.  In the second system,
{\sc Genex II}, the domain knowledge is represented explicitly, and is
thus easily extensible and available for generating explanations.

Because rules {\it match} the current problem against a set of
predetermined situations, the number of problems that {\sc GENEX I}
could solve was limited to those which match the situations
predetermined by the rules.  In the {\sc Genex II}, the system creates
possible models of the current problem and tests each one for
consistency with the observed behavior of the operon \footnote{This
method is known as {\it generate and test}.}.  Theoretically, any
problem derivable from the model can be handled by the second program.

As always, there are tradeoffs.  {\sc Genex II} required a more
complicated control structure to reduce the amount of search.  Its
more detailed knowledge created more steps in the inference chains,
resulting in a slower-executing program.  The performance of the system
would be improved if it could use both heuristic and model-based
reasoning.
 
The {\sc Genex} programs demonstrate that it is possible to apply artificial
intelligence successfully to the solution of certain types of problems
in molecular genetics.  Problems in molecular genetics are complicated
by uncertainty introduced when reasoning about conformations.
Although a definitive solution to this problem must await advances in
protein chemistry, {\sc Genex II} can reduce the number of possible
solutions that must be verified by formulating likely models from the
behavior of the system it is examining.

{\sc Genex II} now reasons about bacterial operons, a small subpart of
molecular genetics.  If a future version of {\sc Genex} is to
represent other systems, such as eukaryotic operons, and use more of
the available knowledge in the field, it will require the use of the
AI techniques discussed here in order to reduce the complexity of the
system.

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