Math 4500, Fall 2017

Math 4500, Fall 2017

"Mathematics is biology's next microscope, only better; Biology is mathematics' next physics, only better." --Joel E. Cohen

"All models are wrong, but some are useful." --George E. P. Box

About the class

This class will be an introduction to mathematical modeling with a particular focus on mathematical biology. We will sample from a variety of problems and modeling techniques throughout the class. Unlike most undergraduate math classes, the scope of this class will be more about breadth than depth.

We will begin with some classical models such as the logistic and predator-prey models for population growth and the SIR model in epidemiology. The second half of the class will be spent learning about a relatively new but widely popular trend of discrete modeling. In particular, the field of mathematical biology has been transformed over the past 15 years by researchers using novel tools from discrete mathematics and computational algebra to tackle old and new problems. These ideas have impacted a wide range of topics such as gene regulatory networks, RNA folding, genomics, infectious disease modeling, phylogenetics, and ecology networks and food-webs. In some cases they have even spawned completely new research areas. This is approach is arguably more accessible and appealing to many scientists and engineers, encouraging cross-disciplinary communication and collaborations.





Lecture notes

Part I. Differential and difference equations
  1. Introduction to modeling. 4 pages (handwritten). Updated Jan 22, 2013.
  2. Difference equations. 12 pages. Updated Jan 12, 2015.
  3. Analyzing nonlinear models. 4 pages (handwritten). Updated Jan 22, 2013.
  4. Models of structured populations. 8 pages. Updated Jan 21, 2015.
  5. Predator-prey models. 11 pages. Updated Jan 28, 2015.
  6. Infectious disease modeling. 12 pages. Updated Feb 9, 2015.
  7. Modeling biochemical reactions. 10 pages. Updated Feb 4, 2015.
Part II. Discrete and agent-based models
  1. Cellular automata and agent-based models. 18 pages. Updated February 11, 2015.
  2. Boolean models of the lac operon in E. coli. 43 pages. Updated February 8, 2017.
  3. Bistability in ODE and Boolean network models. 28 pages. Updated Mar 09, 2017.
  4. Dilution, degradation, and time delays in Boolean network models. 18 pages. Updated Mar 08, 2017.
  5. Reduction of Boolean network models. 18 pages. Updated Oct 31, 2017.
  6. Reverse engineering using computational algebra 29 pages. Updated Nov 10, 2017.
Part III. Stochastic models: genetics, nucleic acids and phylogenetics
  1. CpG islands and hidden Markov models. 12 pages. Updated October 28, 2016.
  2. Hidden Markov models and dynamic programming. 12 pages. Updated April 19, 2017.
  3. Combinatorial approaches to RNA folding. 16 pages. Updated April 15, 2016.
  4. RNA folding via energy minimization. 15 pages. Updated April 15, 2016.
  5. RNA folding via formal language theory. 14 pages. Updated April 15, 2016.