Interpreting T-cell search "strategies" in the light of evolution under constraints.

Two decades of in vivo imaging have revealed how diverse T-cell motion patterns can be. Such recordings have sparked the notion of search "strategies": T cells may have evolved ways to search for antigen efficiently depending on the task at hand. Mathematical models have indeed confirmed that several observed T-cell migration patterns resemble a theoretical optimum; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell's chance of finding antigen. But the same behaviours could also arise simply because T cells cannot follow a straight, regular path through the tight spaces they navigate. Even if T cells do follow a theoretically optimal pattern, the question remains: which parts of that pattern have truly been evolved for search, and which merely reflect constraints from the cell's migration machinery and surroundings? We here employ an approach from the field of evolutionary biology to examine how cells might evolve search strategies under realistic constraints. Using a cellular Potts model (CPM), where motion arises from intracellular dynamics interacting with cell shape and a constraining environment, we simulate evolutionary optimization of a simple task: explore as much area as possible. We find that our simulated cells indeed evolve their motility patterns. But the evolved behaviors are not shaped solely by what is functionally optimal; importantly, they also reflect mechanistic constraints. Cells in our model evolve several motility characteristics previously attributed to search optimisation—even though these features are not beneficial for the task given here. Our results stress that search patterns may evolve for other reasons than being "optimal". In part, they may be the inevitable side effects of interactions between cell shape, intracellular dynamics, and the diverse environments T cells face in vivo. Author summary: T cells are immune cells with an astounding ability to move nearly anywhere in the body. This motion helps them detect and clear up pathogens, and understanding it is key to understanding T-cell immunity. Importantly, the continuous search for pathogens means that T cells face different challenges throughout their lifetime: their needle-in-a-haystack quest for the first signs of disease in lymph nodes differs greatly from their motion in an infected lung, or from how they patrol the skin to guard against future reinfections. These observations have raised the intriguing question: have years of evolution equipped T cells with distinct search "strategies", optimized for whichever searching tasks they might encounter? Although several studies have addressed this question in mathematical models, to date, none have explicitly considered the evolutionary process itself. Here, we directly simulate evolutionary optimization of T-cell search. We find that explicitly simulating "survival of the fittest searchers" can shed new light on why T cells move the way they do. Importantly, we find that the evolving movement patterns are only in part optimized "strategies"—while other parts may merely be "side effects" stemming from the constraints arising from the cell's molecular motor acting in a maze-like environment. [ABSTRACT FROM AUTHOR]