28 February 2025
Zev Gartner
School of Pharmacy, UCSF
Tissues are wildly complex, with properties that emerge from the interactions of large numbers of cells comprising a dizzying number of heterogeneously expressed gene products. The tools of genomics and big data are increasingly viewed as the solution to understanding this complexity. While the utility of these approaches are undeniable, we are exploring a parallel approach. Using bioengineering tools and human mammary organoids as a model system, we provide evidence that the conceptual tools of equilibrium statistical mechanics can provide surprisingly accurate predictions of steady-state phenomena at the tissue scale from only three measurable parameters — an active surface energy, the magnitude of active mechanical fluctuations, and a configurational entropy associated with composing a tissue from different populations of cells. From these measurements we predict the average structure of a tissue across a range of conditions as well as its microscopic variability. This conceptual formalism also provides insight into how changes to these parameters can drive corresponding changes in tissue structure, for example during development and breast cancer progression. I will discuss some assumptions and limitations of this approach, possible extensions to other systems, and the potential to understand other emergent properties of tissues such as cell plasticity and structural phase transitions.