Biophysicist (Rockville, Md.)最新文献

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

Recruiting talented high school and college students to consider a career in the biomedical or biophysical sciences is important, yet often difficult. Encouraging students in regions like Appalachia adds additional challenges due to socioeconomic hurdles and misperceptions. This brief report contains the reflections of a research mentor engaging with students as a high school physics teacher, a principal investigator at research-intensive university, and as a principal investigator at a predominantly undergraduate-focused research university, as well as the viewpoint of a former undergraduate student in the mentor's lab. Different hurdles stand in the way of success at each level. A key issue at the high school level is engaging students in 'real science', the discovery of new knowledge and ideas. With undergraduate students at a larger research institution, a key issue is for the student to have opportunities to engage in meaningful scientific research. At a smaller and more rural research institution, especially in Appalachia, many students have socioeconomic concerns and misconceptions of what scientific careers entail. Regardless of background and environment, there are certain students who thrive on the scientific curiosity to discover new things. All they need is that opportunity to engage in meaningful scientific discovery to become interested in a scientific career.