One of the eight Next Generation Science Standards (NGSS) scientific practices is using mathematics and computational thinking (CT). CT is not merely a data analysis tool, but also a problem-solving tool. By utilizing computing concepts, people can sequentially and logically solve complex science and engineering problems. In this article, we share a successful lesson using protein synthesis to teach CT. This lesson focuses primarily on modeling and simulation practices with an extension activity focusing on the computational problem-solving practices of CT. We identify and define five CT concepts within the aforementioned practices that form the foundation of CT: algorithm, abstraction, iteration, branching, and variable. In this lesson, we utilize a game to familiarize students with CT basics, and then use their new CT foundation to design, construct, and evaluate algorithms within the context of protein synthesis. As an optional extension to the lesson, students enter the problem-solving environment to create a program that translates mRNA triplet codons to an amino acid chain. We argue that biology classrooms are ideal contexts for CT learning because biological processes function as a system, and understanding how the system functions requires algorithmic thinking and problem-solving skills.

Diffusion and osmosis are important biological concepts that students often struggle to understand. These are important concepts because they are the basis for many complex biological processes, such as photosynthesis and cellular respiration. We examine a wide variety of representations used by experienced teachers to teach diffusion and osmosis. To help teachers select appropriate representations for their students, we briefly describe each representation and discuss its pros and cons. After teachers select representations, we offer recommendations for sequencing them. We recommend beginning with macroscopic-level representations that easily allow students to visualize the phenomenon, then moving to microscopic-level representations (cell-level), and finally exploring the phenomenon at the molecular level using virtual representations.