In vivo single-cell ribosome profiling reveals cell-type-specific translational landscapes across the epidermis and uncovers selective reprogramming of AP-1 subunit translation in aged stem cells, lin...

A large-scale proteomics analysis of the dark proteome by the TransCODE Consortium reveals many translated non-canonical open reading frames to encode microproteins and peptideins.

The Sendoel lab studies the role of translational control in health and disease. Our lab employs a variety of in vivo techniques to investigate stem cell behavior in homeostasis, clonal expansions and...

Mathematics has long provided a quantitative framework to interpret cancer biology and treatment. Driven by richer biological and clinical data, the field has evolved into a multidisciplinary and translational endeavor - giving rise to Mathematical Oncology. Yet, the field's strong interdisciplinarity obscures a comprehensive view of its evolution, as well as the boundaries between Mathematical Oncology and adjacent domains. Here, we address this gap through a comprehensive bibliometric analysis spanning 75 years of mathematical research in oncology. Using a manually curated corpus (~1,500 papers) and a large query-based dataset (~19,000 papers), we map the field's conceptual and collaborative development over time. Independent analyses reveal sustained growth, high impact, and pronounced interdisciplinarity, together with a gradual shift from fundamental cancer biology toward therapeutic modeling. This reorientation underpins the emergence of Mathematical Oncology as a distinct field, separate from Systems Biology and Pharmacokinetics / Pharmacodynamics. We show how diverse concepts interlace within Mathematical Oncology, bridging applied mathematics, optimal control, evolutionary theory, and imaging. Collectively, we demonstrate that Mathematical Oncology is not merely the application of mathematics to cancer, but the use of interpretable models integrating clinical, biological, and physical knowledge to improve screening, understand disease evolution, guide therapy, and strengthen forecasting. ### Competing Interest Statement The authors have declared no competing interest. NCI, U54CA274507 Wenner-Gren Stiftelserna/the Wenner-Gren Foundations, WGF2022-0044 Cancer Prevention Research Institute of Texas, RP220225 National Science Foundation, DMS 2436499 MICIU/AEI/10.13039/501100011033 and ERDF/EU, PID2023-146347OA-I00 MICIU/AEI/10.13039/501100011033 and ESF+, RYC2022-036010-I

Our lab at the University of Zurich studies the regulation of gene expression in stem cells and cancer. We are seeking a highly motivated and independent bioinformatician or postdoctoral researcher to...

Scientists have long debated the origins of exceptional human achievements. This literature review summarizes recent evidence from multiple domains on the acquisition of world-class performance. We re...

Comprehensive single-cell transcriptomic analysis of 35 human tissues reveals 12 cross-tissue coordinated cellular modules that exhibit intramodule communication and dynamic changes with ageing, and w...