Teaching Metabolism in Upper-Division Undergraduate Biochemistry Courses using Online Computational Systems and Dynamical Models Improves Student Performance.

Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of metabolic networks. However, multiple professional societies have recognized that most undergraduate biochemistry students acquire only a surface-level understanding of metabolism. We hypothesized that guiding students through interactive computer simulations of metabolic systems would increase their ability to recognize how individual interactions between components affect the behavior of a system under different conditions. The computer simulations were designed with an interactive activity (i.e., module) that used the predict-observe-explain model of instruction to guide students through a process in which they iteratively predict outcomes, test their predictions, modify the interactions of the system, and then retest the outcomes. We found that biochemistry students using modules performed better on metabolism questions compared with students who did not use the modules. The average learning gain was 8% with modules and 0% without modules, a small to medium effect size. We also confirmed that the modules did not create or reinforce a gender bias. Our modules provide instructors with a dynamic, systems-driven approach to help students learn about metabolic regulation and equip students with important cognitive skills, such as interpreting and analyzing simulation results, and technical skills, such as building and simulating computer-based models.

ccNetViz: a WebGL-based JavaScript library for visualization of large networks.

SUMMARY: Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. AVAILABILITY AND IMPLEMENTATION: The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.