Björn Forsberg

About the research

In the lab, we use spatial filtering and metrics of local correlation to estimate the source of variation in ensemble data. We apply this directly to distinguish sources of variation in cryo-electron microscopy data, attribute it to causal mechanisms and describe molecular biology better than previously possible.

Structural biology helps us understand how life works at the molecular level. It does this by showing us the three-dimensional form and function of small biological molecular machines such as proteins and DNA. This knowledge is crucial for understanding how our bodies work and for creating new medicines. One of the most important tools in structural biology is called cryo-electron microscopy (cryo-EM) and uses powerful computers to create 3D models of molecules. Cryo-EM can now also see protein inside cells, study how cells function in much greater detail, and understand how proteins function and how cells react to viruses and bacteria. This can greatly improve our understanding of biology and help us develop better drugs. For cryo-EM to be able to carry out ever larger and more in-depth investigations, however, new calculation tools are needed that better understand the enormous amounts of data that are input and can carry out faster and more systematic investigations. By extension, this can create new diagnostic methods that can see how the body heals and regenerates after injuries or non-genetic diseases such as Alzheimer's, which is already studied with the help of cryo-EM by seeing how molecules get the wrong shape in tissue taken from patients.