Two kinds of knowledge in scientific discovery.

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge-lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time.

Ability, breadth, and parsimony in computational models of higher-order cognition.

Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria.

Constructing explanatory process models from biological data and knowledge.

Objective: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation.

Methods: We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input.

Incorporating biological knowledge into evaluation of causal regulatory hypotheses.

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources.

Guiding revision of regulatory models with expression data.

BioLingua is a computational system designed to support biologists' efforts to construct models, make predictions, and interpret data. In this paper, we focus on the specific task of revising an initial model of gene regulation based on expression levels from gene microarrays. We describe BioLingua's formalism for representing process models, its method for predicting qualitative correlations from such models, and its use of data to constrain search through the space of revised models.