The NLM Colloquia on Biomedical Data Science and Computational Biology Research is a regularly scheduled series of scientific lectures presented by the NLM Division of Intramural Research (DIR), a premier hub of innovation for computational biology and biomedical data science.
The NLM DIR invites experts from outside NLM to present at the Colloquia, where they can share their insights with research communities across NIH and worldwide in the rapidly evolving fields of biomedical data science and computational biology research, as well as how their work impacts these topic areas.
2026 NLM Colloquia Series
Single-Cell Cartography of Plasticity: Transposing Perturbations onto Unseen Phenotypes
Event Date: Friday, May 29, 2026
Time: 11:00am–12:00pm
Speaker: Yogesh Goyal, PhD
Location: The Lister Hill Center Auditorium (LHC, building 38A), and virtual via MS Teams
Abstract:
Single-cell variability within genetically identical populations can produce divergent cell fates under identical perturbations, a phenomenon broadly referred to as phenotypic plasticity and most clearly seen in cancer, where rare cells evade therapy and seed resistance. In this talk, I will describe our work on single-cell cartography of plasticity, using lineage tracing to map and track plasticity and its clonal manifestations over time, and to define the landscape on which heterogeneous fate decisions arise. I will then show how perturbation datasets can be leveraged to transpose functional signals learned in one context onto unseen phenotypes in another. My group’s work provides a general framework for using disparate datasets and systems to study cell-fate variability in cancer and in other settings such as differentiation and reprogramming.
Speaker Bio:
Yogesh Goyal, PhD, is an Assistant Professor of Cell and Developmental Biology and Chemical and Biological Engineering at Northwestern University. His research integrates single‑cell genomics, live‑cell imaging, and quantitative modeling to reveal how cells make fate decisions and adapt to stress during development, regeneration, and disease. Dr. Goyal earned his PhD in Chemical and Biological Engineering at Princeton University, where he investigated how dynamic chemical signals guide animal development. He then completed postdoctoral training at the University of Pennsylvania. As a Schmidt Science Fellow at Penn, he transitioned into single‑cell biology, focusing on how cells organize, communicate, and respond to pathogenic perturbations. The Goyal Lab combines theory, computation, and single‑cell–resolved experiments to track and control cellular plasticity and fate choices in health and disease, with a particular emphasis on cancer. Dr. Goyal is deeply committed to crossing disciplinary boundaries and collaborating broadly across scientific fields.
How to Join:
Location: The Lister Hill Center Auditorium (LHC, Building 38A)
This talk will also be broadcast live: MS Teams
Interpreting services are available upon request. Individuals with disabilities who need reasonable accommodation to participate in this lecture should contact NLMColloquia@nih.gov or the Federal Relay (1-800-877-8339).
Questions during the presentation can be sent to: NLMColloquia@nih.gov.
Sponsored by:
Richard Scheuermann, PhD
Scientific Director, Division of Intramural Research, National Library of Medicine
Exploring the Protein Universe, from Structural Motifs to Proteome-Scale Complexes
Event Date: Tuesday, June 2, 2026
Time: 11:00am–12:00pm
Speaker: Martin Steinegger, PhD
Location: The Lister Hill Center Auditorium (LHC, building 38A), and virtual via MS Teams
Abstract:
The rapid rise of highly accurate structure predictors has transformed our ability to explore proteins at a global scale. With billions of predicted structures available, new opportunities emerge to study not only monomers, but also multimer organization, conserved structural motifs, and protein–protein interfaces. In this talk, I will present our fast computational methods Foldseek-Multimer, Folddisco, and Foldseek-Interface, which enable large-scale search, comparison, and clustering across these structural layers. These methods, combined with MMseqs2-GPU and ColabFold, underpin a major expansion of the AlphaFold Database to proteome-scale quaternary structures, revealing emergent biology that is absent from monomeric predictions alone. By integrating multimeric assemblies, structural motifs, and interaction interfaces, we can map functional space, annotate previously uncharacterized proteins, and gain new insights into protein evolution and biological mechanisms.
Speaker Bio:
Dr. Steinegger is an Associate Professor in the Biology Department at Seoul National University, with a joint appointment to the Interdisciplinary Program in Bioinformatics. He conducted his doctoral studies at the Max Planck Institute for Biophysical Chemistry and was awarded a Ph.D. in computer science with summa cum laude honors from the Technical University Munich in 2018, followed by a postdoctoral fellowship at Johns Hopkins University School of Medicine. Dr. Steinegger’s work covers a wide range of topics in bioinformatics, from detecting genomic assembly contamination to organizing the protein structure universe. He was awarded the Overton Prize for outstanding contributions to computational biology by the International Society for Computational Biology in 2024, the Asan Young Medical Scientists Award in 2026, and was named a Clarivate Highly Cited Researcher (2024, 2025). Dr. Steinegger is an advocate for internationality at his home institution, open science and open source.
How to Join:
Location: The Lister Hill Center Auditorium (LHC, Building 38A)
This talk will also be broadcast live: MS Teams
Interpreting services are available upon request. Individuals with disabilities who need reasonable accommodation to participate in this lecture should contact NLMColloquia@nih.gov or the Federal Relay (1-800-877-8339).
Questions during the presentation can be sent to: NLMColloquia@nih.gov.
Sponsored by:
Richard Scheuermann, PhD
Scientific Director, Division of Intramural Research, National Library of Medicine