New Course Announcement:

AMTH 464/664, Topics in Computational Biology

An overview of basic topics in computational
biology spanning scales from molecules through cells to networks.
It is intended for students with mathematical and/or computational
background to learn selected topics in computational biology, and for
students in biology with some background in mathematics or physics
to learn selected topics in mathematical modeling and computation.

Instructors:

     Director,  Steven W. Zucker, Applied Math and CS
     Lecturers: Thierry Emonet, MCDB
                Michael Krauthammer, Pathology
                Xiao-Jing Wang, Neuroscience
                Gunter Wagner, EEB

When: Mon & Wed, 2:30 - 3:45
Where: DL 220


Computational analysis and modeling is playing an increasingly central
role in all aspects of modern biology. In this class we will introduce a
wide range of subjects, from molecules to systems, and will provide an
overview of techniques, from dynamical systems theory, discrete
mathematics, graph theory, harmonic analysis, stochastic processes and
differential geometry. But in each case we shall adhere to the theme
of how a network architecture informs or constrains biological
function and how network dynamics informs behavior.

The main topics to be covered will include:

1. Escheria coli: how a biochemical network controls cell motility. (Emonet)
2. Molecular interaction networks. (Krauthammer)
3. Evolution of complex morphology: from genes to phenotypes. (Wagner)
4. Perceptual neuroscience: how networks of neurons cooperate to
perceive the world. (Zucker)
5. Cognitive Neuroscience: how networks of neurons make decisions. (Wang)
6. Pattern formation in biology: from ocular dominance bands in brains
to zebra's stripes. (Zucker)

Outline Syllabus:

Week 1. Introduction (SZ)
    2. Enzymatic/biological switches/signal
       transduction/bacterial chemotaxis. (TE)
    3. Signal detection by receptor clusters and bacterial
       chemotaxis. (TE)
    4. basic population genetic theory and robustness. (GW)
    5. configuration space models, network models of gene regulation,
       evolution of robustness. (GW)
    6. Molecular networks and disease genes. (MK)
    7. Pattern formation in biology. (SZ)
    8. Introduction to neuroscience (SZ)
    9. Visual information processing (SZ)
    10. Neural dynamics, attractor networks and memory (XJW)
    11. Neural integrators and decision making. (XJW)
    12. Robustness in computational biology (group)
    13. Project presentations.


Each of the main topics will have two readings: an introductory one and an advanced
one. The advanced articles will be determined at the time of the class.

Examples include:

1. Selected chapters from Berg, Random Walks in Biology.
2. Selected chapters from Dayan and Abbott, Theoretical Neuroscience
3. Selected chapters from Murray, Mathematical Biology