The vision of ISC must evolve with the changing terrain of interdisciplinary science. At the present moment we see a unique opportunity to build on the success of the original sequence and carry the landmark program forward into the next decades in the strongest possible way. Some of the introductory overlap between traditional disciplines is now represented in courses in neuroscience and engineering. However, we see the need for a renewed focus on the intersection of biology, chemistry and data science.

As computational tools and large datasets open up new avenues of research in diverse and complex systems, there is a greater need for ISC-trained undergraduate students in the life sciences than ever before. The new sequence will stay true to the vision of ISC while specifically attracting students interested in complex problems in living organisms. We believe that incipient areas of contemporary research, many in the womb of today’s molecular biology and chemistry departments, will require a rigorous, early introduction to computational and statistical methods in addition to a cohesive understanding of physical principles.

Faculty members who will teach the new ISC sequence are using Academic Year 2023–2024 to develop a course that will better serve the educational mission of the university and the aspirations of our undergraduate students in years to come. We believe that renewing our commitment to the relevance and vitality of the program will ensure that Princeton stays at the leading edge of science education while continuing to bring talent to the frontiers of scientific research.

The new sequence, to be launched on the twentieth anniversary of the original curriculum, will be a revitalized service course for first-year students interested in scientific studies of the natural world. It will fulfill introductory requirements in chemistry and molecular biology while offering students a concrete foundation in calculus-based physics. In addition, we will develop the computational component of the course from scratch, placing an emphasis on scientific computing and data-oriented thinking.