After looking at this a bit more, here's something interesting. First, it's a very physical model, treating cells as 3D entities with 2D interactions (cell surf

After looking at this a bit more, here's something interesting. First, it's a very physical model, treating cells as 3D entities with 2D interactions (cell surfaces), and they use their measured binding constants (essentially a measure of protein-protein stickiness across all their proteins) as a key parameter, along with measures of protein expression in various immune system cell types. They're classified into: CD4 T cells, Helper CD4 T cells, Natural killer cells, CD8 T cells, Dendritic cells, Monocytes, Regulatory T cells, B cells. This is all quite complex, here's a simplified 6 min overview of some of their roles, note the importance of receptor-receptor interactions at every stage: https://www.youtube.com/watch?v=6eMh0Vpyoik https://www.youtube.com/watch?v=6eMh0Vpyoik For a nice visualization of immune cells attacking cancer cells: https://www.youtube.com/watch?v=K09xzIQ8zsg https://www.youtube.com/watch?v=K09xzIQ8zsg So, this is a complex dynamic network, and a key feature of such networks is that a change at one point in the network can percolate across the entire network, which can be critical to their normal functioning, as well a play roles in failure /pathology. The ability to model such casading effects could be game-changing in terms of developing treatments such as targeting cancer cells, blocking autoimmune activation, etc. From supplement: > "Another direct test of the predictive power of this model would be to experimentally block specific cell-surface protein interactions (such as by adding recombinant ligands that competitively inhibit their receptors’ interactions) and gauge how cell-to-cell contact frequencies change. If these changes are predictable from our “ground-up” mass action kinetics model, that would demonstrate the utility of our systematic approach."