An integrated model for interdisciplinary graduate education: Computation and mathematics for biological networks

Common Interests Without Common Expertise: Reflections on Early-career Experiences in Cross-Disciplinary Research

Cross-disciplinary research enables us to tackle complex problems that require expertise from different fields. Such collaborations involve researchers who have different perspectives, communication styles, and knowledge bases, and can produce results far greater than the sum of their parts. However, in an era of increasing scientific specialization, there exist many barriers for students and early-career researchers (ECRs) interested in training and undertaking interdisciplinary research endeavors. This perspective examines the challenges that students and ECRs perceive and experience in cross-disciplinary work and proposes pathways to create more inclusive and welcoming research environments. This work emerges from a National Science Foundation (NSF)-funded workshop held during the Society for Integrative and Comparative Biology (SICB) Annual Meeting in January 2023 in Austin, TX. The workshop brought together seasoned interdisciplinary scientists with undergraduate and graduate students to identify and discuss perceived challenges through small group discussions and experience sharing. Through summarizing a range of student concerns about embarking on careers as interdisciplinary scientists and identifying ways to dismantle institutional and lab management-level barriers, we aim to promote an inclusive and collaborative problem-solving environment for scientists of all experience levels.

Prioritization of the concepts and skills in quantitative education for graduate students in biomedical science

Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science. Far less attention has been paid to the graduate curriculum and the particular challenges of the diversity of specialization within the life sciences. We propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities, derived from analysis of the expectations for students in particular programs. Due to the plethora of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We collected key recent papers suggested by the faculty in biomedical science programs, chosen to include important scientific contributions that the faculty consider appropriate for all students in the program to be able to read with confidence. The quantitative concepts and methods inherent in these papers were then analyzed and categorized to provide a rational basis for prioritization of those concepts to be emphasized in the education program. This novel approach to prioritization of quantitative skills and concepts provides an effective method to drive curricular focus based upon program-specific faculty input for science programs of all types. The results of our particular application to biomedical science training highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty. There was little reference in the key recent papers chosen by faculty to classic mathematical areas such as calculus which make up a large component of the formal undergraduate mathematics training of graduate students in biomedical areas.

Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives

Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.