Prerequisites: Physics 101 (or higher), Math 104 and [114 or 115]. Recommended: previous or concurrent Physics 102; basic background in chemistry and biology.
Every week we hear some highly-placed pundit announcing the end of the qualitative era in life science, and the need to train future scientists in mathematical modeling methods. Normally missing from such pronouncements are issues like "What is a physical model, anyway?" and "How do we know when a simple, reductionistic modeling approach is appropriate/inappropriate?"
The course will address such questions by looking at some classic case studies of successful reductionistic models of complex phenomena. At its best, such modeling brings out emergent properties of systems, i.e. those which are largely independent of specific details and cut across different classes of organisms. The physical viewpoint can also help us to find links between apparently unrelated phenomena.
We'll emphasize the key steps of (1) making estimates, often based on dimensional analysis, (2) using them to figure out which physical variables and phenomena will be most relevant to a given system, and which may be disregarded, and (3) finding analogies to purely physical systems whose behavior is already known.
We'll study basic biological processes, mainly at the molecular and cellular level, in the light of simple ideas from physics. Topics may include entropic forces, free energy transduction at the molecular level, the structure of biopolymers, molecular motors, pattern formation (oscillation and morphogenesis), immune response, nerve impulses and neural computing, and other forms of signal transduction.