BioGraph

Biograph is a collaborative learning opportunity based on an NSF-funded, multi-year research study being carried out by the MIT Scheller Teacher Education Program and the University of Pennsylvania Graduate School of Education.The goal of Biograph is to improve introductory biology learning at the high school level by introducing complex systems topics as a unifying theme and encouraging students to interact with agent-based computer models designed to ‘bring to life’ complex systems ideas in biology. It is our hypothesis that this improved curriculum will, ultimately, better prepare students for college level instruction, and help to address common misconceptions in the biological sciences.

"Complex Systems is a new field of science studying how parts of a system give rise to the collective behaviors of the system, and how the system interacts with its environment. Social systems formed (in part) out of people, the brain formed out of neurons, molecules formed out of atoms, the weather formed out of airflows are all examples of complex systems. The field of complex systems cuts across all traditional disciplines of science, as well as engineering, management, and medicine." (excerpt from NECSI web site)

How will complex systems thinking and agent-based computer models help my students learn biology? In a typical high school biology class, students are often taught about how the individual agents in biological systems act, without addressing how their movements and interactions (multiplied by thousands and millions of agents performing actions at the same time, in parallel) result in the behavior of biological systems as a whole.

For example, we teach students that enzymes are necessary in the transformation of milk into cheese (curds and whey). But we don’t think about how it all actually happens -- do enzymes and milk ‘know’ what to do to create cheese? How does the actual change take place?

Agent-based computer models provide a way to visualize and study complex systems. An agent-based model consists of a virtual world filled with ‘agents.’ Agents are like creatures that follow simple rules. They can represent any kind of individual: people, cars, atoms, rabbits, cells etc. Complex systems can be modeled by giving the agents a set of rules for how to behave and interact. The system-level results are often surprising.