title: Modeling collective & intelligent decision making of multi-cellular populations.
abstract: In the presence of unpredictable disturbances and uncertainties, cells intelligently achieve their goals by sharing information via cell-cell communication and making collective decisions, which are more reliable compared to individual decisions. Inspired by adaptive sensor network algorithms studied in communication engineering, we propose that a multi-cellular adaptive network can convert unreliable decisions by individual cells into a more reliable cell-population decision. It is demonstrated using the effector T helper (a type of immune cell) population, which plays a critical role in initiating immune reactions in response to invading foreign agents (e.g., viruses, bacteria, etc.). While each individual cell follows a simple adaptation rule, it is the combined coordination among multiple cells that leads to the manifestation of "self-organizing" decision making via cell-cell communication.
authors.forename: Yong-Jun
authors.initials: YJ
authors.lastname: Shin
authors.forename: Bahareh
authors.initials: B
authors.lastname: Mahrou
mesh_terms: D000465:Algorithms; D002450:Cell Communication / Q000187:drug effects; D002452:Cell Count; D004058:Diffusion; D007109:Immunity / Q000187:drug effects; D007376:Interleukin-2 / Q000494:pharmacology; D008954:Models, Biological; D013601:T-Lymphocytes / Q000166:cytology / Q000187:drug effects
chemical_list: D007376:Interleukin-2