Biology is designed for multi-semester biology courses for science majors. It is grounded on an evolutionary basis and includes exciting features that highlight careers in the biological sciences and everyday applications of the concepts at hand. To meet the needs of today’s instructors and students, some content has been strategically condensed while maintaining the overall scope and coverage of traditional texts for this course. Instructors can customize the book, adapting it to the approach that works best in their classroom. Biology also includes an innovative art program that incorporates critical thinking and clicker questions to help students understand—and apply—key concepts.

By the end of this section, you will be able to:Discuss the concept of entropyExplain the first and second laws of thermodynamics

By the end of this section, you will be able to:Discuss the concept of entropyExplain the first and second laws of thermodynamics

Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.

In this online activity, learners discover how random variation influences biological evolution. Biological evolution is often thought of as a process by which adaptation is generated through selection.Œć While it is recognized that random variation underlies the process, emphasis is usually placed on selection and resulting adaptation, leaving a sense that it is selection that drives evolution.Œć This simulation highlights the creative role of random variation, offering a somewhat different perspective: that of evolution as open-ended exploration driven by randomness and constrained by selection, with adaptation as a dynamic, transient consequence rather than an objective.

Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.

This model-eliciting activity has students create rules to allow them to judge whether or not the shuffle feature on a particular iPod appears to produce randomly generated playlists. Because people's intuitions about random events and randomly generated data are often incorrect or misleading, this activity initially focuses students' attention on describing characteristics of 25 playlists that were randomly generated. Students then use these characteristics to come up with rules for judging whether a playlist does NOT appear to be randomly generated. Students test and revise their rules (model) using five additional playlsits. Then, they apply their model to three particular playlists that have been submitted to Apple by an unhappy iPod owner who claims the shuffle feature on his iPod is not generating random playlists. In the final part of the activity, students write a letter to the ipod owner, on behalf of Apple, explaining the use of their model and their final conclusion about whether these three suspicious playlists appear to have been randomly generated.This lesson provides an introduction to the fundamental ideas of randomness, random sequences and random samples.

Building on their understanding of graphs, students are introduced to random processes on networks. They walk through an illustrative example to see how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students. This demonstrates how scientists and engineers use mathematics to model and simulate random processes on complex networks. Topics covered include random processes and modeling disease spread, specifically the SIR (susceptible, infectious, resistant) model.