Protein behavior linked to cancer-causing mutations

Unprecedented multiscale model reveals importance of lipids

February 1, 2022

Placeholder Image — In the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), the macroscale simulation runs a large system, with hundreds of proteins, at low resolution. Machine learning decides which regions of the macro-model require investigation in a microscale simulation at much higher resolution. Analysis from this microscale simulation is fed back into the macroscale model to improve its fidelity. Credit to: Tim Carpenter, Lawrence Livermore National Laboratory

Feb. 4 is World Cancer Day, an international awareness event to inspire action in the fight against cancer.

Using science to create new possibilities in this fight, Los Alamos National Laboratory scientists and partners at Lawrence Livermore National Laboratory and the National Cancer Institute’s Frederick National Laboratory for Cancer Research recently produced a new model that uses computer simulation to understand the disease. The collaborators’ work revealed the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.

Read about this multiscale model and the team’s findings.