Researchers from the University of Southern California (USC) have launched a global scientific effort to find the next diabetes drug by creating a comprehensive model of a cell that is central to the disease.
Diabetes
According to the World Health Organisation (WHO) cases of diabetes have more than tripled globally since 1980, increasing from an estimate 100 million to more than 400 million.
In a commentary recently published in the journal Cell, representatives of the new Pancreatic Beta Cell Consortium at USC made a ‘call to arms’ inviting scientists from all over the world to join their cause and help model the cell at an atomic scale and whole-cell scale. Completion of the project is anticipated within five years.
“We are converging to solve a difficult problem to solve a structure at multiple scales, from the individual atoms, to the small molecules, to the macromolecule, to the cell,” said Raymond Stevens, a USC chemist and structural biologist who is the lead author on the work and a founder of the consortium.
The global effort will involve experts in the fields of biology, chemistry, computational biology (modelling), engineering, mathematics and imaging. Additionally, the group includes artists and filmmakers.
This convergent scientific effort will be launched at the Bridge Institute at USC Michelson Center, where engineers and scientists from three schools at the university — the Dornsife College of Letters, Arts and Sciences, the USC Viterbi School of Engineering and the Keck School of Medicine of USC — are working together to develop new treatments, diagnostics and devices to solve challenges like cancer and Alzheimer’s disease.
Described as ‘crowd-sourced science’ the beta cell project will be the first major cross-disciplinary collaboration at the Michelson Center, which officially opened last November. The project consortium, which includes collaborators from top-ranked institutions is establishing an open data bank to which anyone can deposit research and data about the pancreatic beta cell. Multiple sets of data will be integrated to form the multiscale model of the cell and the entirety of its components, from individual atoms to the nucleus. For quality control, consortium scientists will curate and vet all contributions.
“This is the ultimate modelling problem,” said co-author Andrej Sali, a scientist and bioengineer at the University of California, San Francisco who, as a modeller, integrates data. “The cell is a very big system. It’s not like gas and it’s not like a crystal, but it maximises complexity somewhere in between these two extremes. It’s also hierarchical: atoms, molecules, complexes, organelles and cells. It will have to be solved by an integrative approach that relies on multiple sources of information.”
The choice of cell was carefully considered also, with the pancreatic beta cell leading the way as it has already been extensively studied and is a simple machine — where the input (glucose) equals output (insulin).
“This cell type has such huge medical implications,” said Frank Alber, a co-author of the paper and professor of molecular and computational biology at the Bridge Institute at USC Michelson Center. “You want to use a predictive, whole-cell model in drug design, and you want to see what effects that drug would have, not only on a protein pathway within the cell, but on the whole cell.”