This project will help understand how cells can work together to interpret external signals and collectively make decisions. Earlier work from the Ewald group showed that cells in tissues cooperate to make decisions, such as choosing when and where to move. This cooperation can help make more reliable decisions. One example of this is that when many cells work together to follow a chemical signal, they are more precise and reliable than a single cell on its own. This project studies how this happens, and how cells can work together even when each cell can have highly variable responses to a signal – when cells are individually unreliable. This project has two critical types of experiments – both of which supplemented by computational modeling and mathematical theory. In the first experiment type, a highly variable collection of cells will be created, combined of cells that think they see a signal telling them to move or grow (active cells), and those that don’t (inactive cells). To understand how these cells work together to make decisions, mathematical models will be used to predict the final decision the cells make – the direction of tissue growth – from the initial location of the active cells. In the second experiment type, the cells will be exposed to an external signal. How precisely will branch locations follow this signal? Is this affected by cell variation (different fractions of active cells)? These experimental results and others will be described with computational models that include both the physical forces between cells and the motion of the molecules that the cells are sensing. Understanding how cells cooperate in this way will help us understand how developing embryos are reliably formed – when cells end up in the wrong place, this can lead to birth defects. This project will also support the training of students from the high school level to graduate students. High school students will be recruited from Baltimore schools through the Women in Science and Engineering (WISE) program to participate in research. In addition, students trained through this project will be trained in collaborative writing and outreach.
Cells within tissues and organs cooperate in order to move and sense signals; this cooperation allows them to perform tasks that single cells cannot. A dramatic example, and the major focus of this project, is collective gradient sensing, where groups of cells measure a signal gradient, even when a single cell cannot. Collective gradient sensing involves comparisons between cells, and will be strongly affected by cell-to-cell variability, as even genetically identical cells can have highly variable motilities and responses to signal. Understanding collective gradient sensing will require quantifying these cell-to-cell variations and modeling their consequences. This project is a combined experiment and theory approach to understanding collective gradient sensing and cell-to-cell variation by 1) testing existing hypotheses in mammary organoids where variability can be induced, and 2) developing improved models that address variability in cell motility, cell cycle, and cell division. Collective gradient sensing accuracy should – in theory – depend on a balance of cell variability and the ability of a tissue to re-arrange. Variability and fluidity will be controlled and measured in experiments on branching mammary organoids, which undergo collective gradient sensing. Experiments on organoids will be performed to simultaneously characterize sensing and fluidity to test this idea. In addition, artificial variability will be induced by studying mosaic organoids. These experiments will be analyzed with simple stochastic models previously developed by the Camley group. Motivated by the results of these experiments, new computational models of organoids with cell-to-cell variation in signaling, mechanical, and motility properties will be created using both self-propelled particle and biochemical/mechanical cell models. Estimation theory will be used to find fundamental limits to accurate group decisions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
