Complex system informatics

This is an emergent-content course, with minimal prerequisites, designed to introduce the Bayesian tools of correlation-based complexity in an interdisciplinary way to students in the code-based sciences (including fields that touch molecular biology and computer science) as well as to students seeking modern scientific perspectives on information industries (e.g. journalism) and their effects. The target audience in particular includes: (a) students who in context of their (or their employer's) applications may want to collaborate with informatics professionals, or at least have a sense of what they offer, and (b) students with informatics credentials in their own fields interested in a broader perspective but not yet ready to take prerequisites for informatics work in other disciplines.

The course is designed to take advantage of the fact that information theory perspectives allow one to paint with a unified-brush practical tools (like mutual information) for recognizing and measuring correlations between observations, and thus for example to:

Like other courses on emergent-content, the course is comprised of an evolving collection of discipline-specific modules, tied together with a unifying overview and a stable set of everyday interface-elements.

For this course, the overview begins with questions like: What is information, how is it measured, and where does it reside? The same questions are asked of a correlation between two structures. What other properties of information and correlations can we count on, regardless of the discipline to which they pertain? How does the use of these concepts differ from one discipline to the next? The jargon used to discuss information, as with the jargon used to discuss available-work (information's thermodynamic sister), differs greatly from field to field. The unifying overview will therefore strive to help students understand the jargon of different fields, and thus see connections between them as well.

The remainder of the course will focus on examples of tools currently in use, particularly in computer science, molecular biology, and the study of complex systems. In the University of Missouri system's Sept 2004 "bioinformatics-PhD readiness report", inter-disciplinary methods development and routine research support are described as two separable but complementary goals for informatics instruction. One might in that context say that this course is designed to provide examples of present-day support tools (the latter) plus an integrative gestalt to qualify students individually as cross-disciplinary developers (the former) when they encounter unsolved problems in years ahead.

After a unifying introduction which includes some math useful across disciplines, modules in the course might e.g. be grouped in this way:

Interface elements that have in our experience served effectively for students as "vertebrae" in courses which move from one potentially-unfamiliar field to another include:

Groups elsewhere have offered justification of related courses for both computer science (e.g. MIT) and molecular biology (e.g. NIH) professionals. We offer here a broader integrative scope, with hopefully (in days ahead) a campus-specific agenda that is more down to earth as well.

This is the beginning of a list of tools that might be examined in some detail, mentioned in passing, or not mentioned at all. What informatics tools do you think might be worth spending time on in such an introductory course?
Draft paper and notes from a May 2005 talk at UIUC on the thermal roots of correlation-based complexity.
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