Motto: Well, you've got to start somewhere...
Fall 2011, CRN 88396, 3CR
Tues/Thur 1:00 pm - 2:15 pm in Aderhold Learning Center 12
(Cross-listed with Math 8530, Biol 6930)
Click the thumbnails for the full, letter-sized, PDF posters
Here is the syllabus for this class. As this is the first time this class has been taught, details may vary during the semester. It is intended only as a guide.
I strongly recommend reading the free materials provided by the publisher of Ellner and Guckenheimer's book that we will occasionally use, particularly the Preface and Chapter 1. More math-oriented students can look at the later chapters too, especially Chapter 9. We will not be covering mathematical details in these chapters, just the concepts and diagrammatic structures and relationships.
Also worth looking through are several well-written wikipedia pages:
There will be no formal examinations in the course, and no formal prerequisites (registration will be by instructor consent - please email me with your details and interests). Instead, there will be assessments such as collaborative essay assignments and in-class presentations based on reading assignments.
In this course we will ponder questions such as the following: What is the connection between (a) the ability of an F-16 fighter jet to maneuver much more nimbly than an F-4, (b) the possibility that small genetic modifications lead to large differences in phylogenetic outcome within a couple of generations, and (c) the ability of a neural circuit controlling limb motor patterns to switch an animal rapidly between locomotive gaits?
The class is intended for the non-mathematically literate, and yet we will consider non-elementary concepts from applied mathematics and modeling in the biosciences. A primary goal of this course is to increase basic literacy about modeling and simulation, and to promote future participation in collaborations with mathematicians and computational modelers. You will be motivated to appreciate that there are many insights from mathematical thinking that can improve biologists' ability to comprehend and develop complex theories of biological mechanisms, even if they lack traditional training in mathematics.
Do not mistake this class as a "superficial" approach to the modeling of complex problems. On the contrary, it will complement mathematically rigorous, traditional courses on mathematical and computational modeling by providing students with a basis for evaluating good modeling projects in the literature, and understanding their assumptions and limitations. Most traditional courses are themselves, in that sense, superficial in their treatment of these topics, and take many things for granted. It can lead to both students and professionals making poorly chosen approaches to modeling their pet phenomena. We will not try to digest or review the large literature on any given subject area, which certainly would require more time and a formal training in mathematics.
Necessarily, the tools, techniques, and skills of modeling come secondary to the idea of what a reasonable model might consist of, what assumptions need to be made, what types of data lead to what kinds of model, what kinds of representations and techniques are available, what their scopes are, and so on. These are important meta-discussions that are generally avoided in the education of math-oriented students, and is an important reason why I encourage such students to be in this class too.
We will focus on intuition and qualitative, geometric mathematical concepts. This will include ideas drawn from calculus and statistics (among other technical subjects) but a technical understanding of the math will not be assumed, nor will it be taught. This is not a remedial math course! We will discuss different biological systems that have been the subject of modeling through journal articles and book material. Among other topics, we will discuss evolutionary stable strategies in evolution and its abstract connection to the idea of attractors in brain states, small world and other kinds of structured networks in the brain, pattern formation concepts, rhythms in the nervous system. We will analyze the benefits, misconceptions, limitations, and pitfalls of modeling such systems when guided by experimental data, in terms of both the logic of mechanistic theories and their abstraction to mathematical principles.
As should now be clear from this description, students will not be learning specific computer tools in this class. (I actually plan on running such a class in the new Neuroscience undergrad curriculum, but this is not it.) Nonetheless, students will probably interact with some elementary, pre-packaged simulations to get some visual experience with changing parameters, and so on.
For the visit of Carson Chow from NIH, the class prepared this news item (also here on GSU's front page, Nov 28th 2011) and this handout for the seminar to help the Biology department's graduate student attendees.
We will use Google Docs and Google Groups to manage the collaborative writing project. The link for active class members is this.
Features in GDocs are live updates as collaborators write, live chat between collaborators, records (provenance / audit trail) of edits especially in case of accidental loss of material, export options to PDF and DOC, multiple formats for materials including document text, presentation slides, and drawings.