"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

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.

- Course Syllabus
- Biological Feedback (book, pdf version), by René Thomas and Thomas D'Ari, 1990 (updated 2006).
*Simple mathematical models with very complicated dynamics*, by Robert May, published in*Nature*, 1976- Animated gif of cobwebbing in the logistic map. Compare to the bifurication diagram. Both of these from Wikipedia
- MATLAB files for cobwebbing, a single species population model the predator-prey model, and the SIR model. Written by Elizabeth Allman and John Rhodes, authors of Mathematical Models in Biology
- If smallpox strikes Portland C.L. Barrett,
S.G. Eubank, J.P. Smith.
*Scientific American*, Vol. 292 (2005), pp. 54-61. - Michaelis-Menten kinetics
- 2-minute video on gene expression
- John Conway's Game of Life segment, from Stephen
Hawking's
*The Meaning of Life*. - Game of Life applet
- Cellular Automaton Explorer, a free research, teaching, and exploration tool created by David Bahr.
- TED talk by Stephen Wolfram:
*The theory of everything*. - NetLogo, a multi-agent programmable modeling environment.
- Analysis of Dynamic Algebraic Models (ADAM), a web-based software tool for multi-state discrete models of biological networks.
- text file of Boolean lac operon files in polynomial form, for easy entry into ADAM.
- Sage: free open-source mathematics software. Homepage | SageMathCloud
- Sage worksheet:
*lac*operon Boolean network model - In-class worksheet on reverse engineering
- In-class worksheet on RNA folding
- The CpG Educate suite
- Final project ideas.

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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.

1. Cellular automata and agent-based models. 18 pages. Updated February 11, 2015.

2. Boolen network models of gene regulatory networks. 22 pages. Updated February 4, 2016.

3. The

4. Bistability and a differential equation model of the

5. Bistability in Boolean network models. 18 pages. Updated Feb 12, 2016.

6. Reduction of Boolean network models. 18 pages. Updated Feb 18, 2016.

7. Reverse engineering using computational algebra 28 pages. Updated March 4, 2016.

8. Finite dynamical systems and computational algebra. 9 pages (handwritten). Updated Mar 15, 2013.

9. Asynchronous Boolean models of signaling networks. 14 pages. Updated Mar 1, 2016.

1. Combinatorial approaches to RNA folding. 16 pages. Updated April 15, 2015.

2. RNA folding via energy minimization. 15 pages. Updated April 15, 2015.

3. RNA folding via formal language theory. 14 pages. Updated April 15, 2015.