This chapter describes the design of a prototype knowledge-based system for crystal and molecular structure determination from diffraction data. This system enhances current methods for the determination and interpretation of protein structures by incorporating direct methods probabilistic strategies, experience accumulated in the crystallographic databases, and knowledge representation and reasoning techniques for machine imagery.

The protein folding problem has been attacked from many directions. One set of approaches tries to find out correlations between short subsequences of proteins and the structures they form, using empirical information from crystallographic databases. AI research has repeatedly demonstrated the importance of representation in making these kinds of inferences. In this chapter, we describe an attempt to find a good representation for protein substructure. Our goal is to represent protein structures in such a way that they can, on the hand, reflect the enormous complexity and variety of different protein structures, and yet on the other hand facilitate the identification of similar substructures across different proteins.

This article is concerned with the general question of how to represent biological knowledge in computers such that it may be used in multiple problem solving tasks. In particular, I present a model of a bacterial gene regulation system that is used by a program that simulates gene regulation experiments, and by a second program that formulates hypotheses to account for errors in predicted experiment outcomes. This article focuses on the issues of representation and simulation; for more information on the hypothesis formation task see (Karp, 1989; Karp, 1990). The bacterial gene regulation system of interest is the tryptophan (trp) operon of E. coli (Yanofsky, 1981). The genes that it contains code for enzymes that synthesize the amino acid tryptophan.

The use of computers in chemistry and biochemistry has been widespread for many years, with machines performing many complex numerical calculations, e.g.

This chapter will discuss one example of how AI techniques are being integrated with, and extending, existing molecular biology sequence analysis methods. AI ideas of complex representations, pattern recognition, search, and machine learning have been applied to the task of inferring and recognizing structural patterns associated with molecular function. We wish to construct such patterns, and to recognize them in unknown molecules, based on information inferred solely from protein primary (amino acid) sequences.

Our understanding of any process can be measured by the extent to which a simulation we create mimics the real behavior of that process. Deviations of a simulation indicate either limitations or errors in our knowledge. In addition, these observed differences often suggest verifiable experimental hypotheses to extend our knowledge. The biochemical approach to understanding biological processes is essentially one of simulation. A biochemist typically prepares a cell-free extract that can mediate a well-described physiological process. The extract is then fractionated to purify the components that catalyze individual reactions.

Human scientists actively seek out information that bears on questions they have decided to pursue. They design experiments, explore the implications of the knowledge they have, refine their questions and test alternative ideas. Although many discoveries are the result of unexpected observations, these surprises take place in the context of an explicit pursuit of knowledge. Viewing scientific discovery as a kind of motivated action raises some basic issues common to goal-directed behavior generally: Where do desires (to know) come from? What are the actions that can be taken (to discover)? What are the resources those actions consume, and how are they allocated? How are decisions about selecting and combining actions made?

Cells function as organized chemical engines carrying out a large number of transformations, called bioreactions or biochemical reactions, in a coordinated manner. These reactions are catalyzed by enzymes and exhibit great specificity and rates much higher than the rates of non-enzymatic reactions. Enzymes are neither transformed nor consumed, but that facilitate the underlying reactions by their presence. The coordination of the extensive network of biochemical reactions is achieved through regulation of the concentrations and the specific activities of enzymes. Single enzymecatalyzed steps in succession form long chains, called biochemical pathways, achieving the overall transformation of substrates to far removed products.

Cohen, Design Considerations for a Program to Provide Consultations in Clinical Therapeutics, in Proceedings of San Diego Biomedical Symposium, February 1974.

Cohen, Design Considerations for a Program to Provide Consultations in Clinical Therapeutics, in Proceedings of San Diego Biomedical Symposium, February 1974.