References to articles by P.D. Karp
Date: Wed, 10 May 1995 18:10:47 PDT
From: Ron Sapolsky
To: gwelz@panix.com
Subject: Re: Genome Program II
I saw your posts in bionet.genome.chromosomes and what you were discussing
reminds me of the work being done by Peter Karp at SRI. Here's two of his
references from Medline:
1.Title: A knowledge base of the chemical compounds of intermediary metabolism.
Author: Karp PD.
Journal: Computer Applications in the Biosciences, 1992 Aug, 8(4):347-57.
Abstract: This paper describes a publicly available knowledge base of the
chemical compounds involved in intermediary metabolism. We consider the
motivations for constructing a knowledge base of metabolic compounds, the
methodology by which it was constructed, and the information that it
currently contains. Currently the knowledge base describes 981 compounds,
listing for each: synonyms for its name, a systematic name, CAS registry
number, chemical formula, molecular weight, chemical structure and
two-dimensional display coordinates for the structure. The Compound
Knowledge Base (CompoundKB) illustrates several methodological principles
that should guide the development of biological knowledge bases. I argue
that biological datasets should be made available in multiple
representations to increase their accessibility to end users, and I present
multiple representations of the CompoundKB (knowledge base, relational data
base and ASN. 1 representations). I also analyze the general
characteristics of these representations to provide an understanding of
their relative advantages and disadvantages. Another principle is that the
error rate of biological data bases should be estimated and documented-this
analysis is performed for the CompoundKB.
2.Title: Artificial intelligence methods for theory representation and
hypothesis formation.
Author: Karp PD.
Journal: Computer Applications in the Biosciences, 1991 Jul, 7(3):301-8.
Abstract: This article describes artificial intelligence methods for
representing theories in molecular biology, and for improving the
predictive power of these theories using experimental data. A program
called GENSIM provides a framework for representing theories that includes
descriptions of classes of biological objects (genes, enzymes, etc.), and
processes that specify potential interactions among these objects (such as
enzymatic reactions). GENSIM can employ a theory specified within this
framework to predict the outcomes of biological experiments. A program
called HYPGENE comes into play when the observed outcome of an experiment
does not match the outcome predicted by GENSIM. HYPGENE works backward from
the error in GENSIMs prediction to postulate changes to both the theory
embodied by GENSIM, and the presumed initial conditions of the experiment.
I view HYPGENEs hypothesis generation task as a design problem, and I have
adapted AI methods developed for design and planning to this task. These
techniques were developed in conjunction with an in-depth study of the
discovery of the gene regulation mechanism of attenuation in the E. coli
tryptophan operon. Both GENSIM and HYPGENE have been tested on sample
problems from the history of attenuation, and produced many of the same
solutions as biologists did.
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