Addressing Student Diversity and Equity.

Emily sits hunched over her computer, rapidly typing commands. She checks and double-checks the code. Then she executes the program, smiling gleefully as zombies scatter across the screen, growing in number as the population of humans dwindles. "See how quickly the zombie infestation spreads?" she asks excitedly. "That's what happens in real life with a contagious disease. And I can simulate it with a set of simple commands." She turns back to the computer to modify the program before running it again. Meanwhile, Alex rolls a regular pair of dice, identifies the direction and distance of his move, and places a marker on a space on a paper "chessboard." He records the steps and the position in his laboratory notebook, repeats the process, and continues like this until all of the spaces are covered. He is attempting to model with a few simple rules the spread of a population, analogous to an invasive species or a bacterial colony expanding into new territory. What is going on? Is this biology? Mathematics? Computer science? In fact, these students are exploring the emerging field of computational biomodeling. Biomodeling is the study of the structures and behaviors of interacting biological entities such as molecules, cells, or organisms. While physical and chemical processes give rise to various spatial and temporal structures, even the simplest biological phenomenon is infinitely more complex (Kling 2004). Over the past decade, much of biomodeling research has focused on developing mathematical and computer models that replicate the characteristics of a cell or other biological organism (Anderson, Chaplain, and Rejniak 2007; Chauviere and Preziosi 2010). At colleges and universities, mathematicians, biologists, physicists, biochemists, and computer scientists are developing ways to study biological problems using mathematical methods, probability theory, and stochastic (randomly determined but statistically analyzable) processes coupled with computer modeling. Several universities have created multidisciplinary programs that examine this growing field, including the University of Notre Dame, Indiana University, and Ohio State University. Computer modeling programs that simulate biological processes are found online (see "On the web"). This article describes hands-on activities that introduce teachers and students to biomodeling. The activities align with the Next Generation Science Standards (NGSS Lead States 2013; see box, p. 51). Emergent behavior A key concept in computational biomodeling is emergent behavior, in which individual agents work together, and larger patterns arise through interactions among simpler entities that do not exhibit such properties themselves. (An example is a flock of birds moving together without any apparent central coordination or leadership.) With biomodeling, scientists can show that a few simple rules can achieve order from chaos. To develop these models, step-by-step stochastic, probabilistic processes are used to predict long-term behavior. The three activities below are classroom-tested examples of biomodeling concepts that demonstrate simple stochastic processes. Each can be completed in a single class period and requires minimal equipment. All are appropriate for all high school grade levels and biology courses, from introductory biology to AP Environmental Science to AP Biology. (Note: It is not necessary to introduce students to myxobacteria to complete these exercises. They can be performed as inquiry-based exercises with a follow-up classroom discussion of the behavior of Myxococcus xanthus.) [FIGURE 1 OMITTED] [FIGURE 2 OMITTED] Activity 1: Random walk and simulating the movement of myxobacteria in one dimension. Background: M. xanthus are aerobic, rod-shaped bacteria that move across surfaces via gliding motility, i.e., by sliding along a surface using pulling and pushing. …