Bayesian chemotaxis: Information theory of chemotaxis agents combining spatial and temporal gradient-sensing

Biological systems process information despite noise-corrupted input, often operating at physical limits. A prime example is chemotaxis, i.e., active navigation in spatial fields of chemical cues, which enables immune cells to find inflammation sites, sperm cells to find the egg, and bacteria and social amoeba to build communities. Intriguingly, cells of different size use different chemotaxis strategies, comparing extracellular concentrations in either space or time. Only heuristic arguments exist to explain this evolutionary choice.

I will present an information theory of an ideal agent that uses Bayesian inference to fuse data from spatial and temporal gradient-sensing in order to find the source of a diffusing signaling molecule, thus generalizing the information-greedy infotaxis strategy to spatially extended agents. Using information decomposition, we can predict when each strategy provides more information as function of a powerlaw that combines agent size, motility noise and sensing noise into a single predictor. This predictor is consistent with data from 250 chemotactic cells, including unusual bacteria that use spatial gradient-sensing. We demonstrate our theory with a bio-inspired search robot.

If time permits, I will show how chemokinesis, possibly a precursor of chemotaxis, already enables efficient source finding without sensing gradients [2], as well as discuss a biological example of helical chemotaxis used by sperm cells, which represents an intermediate between spatial and temporal gradient-sensing [3]. Our work bridges a gap between extensive previous work that focused exclusively on one strategy, and suggests determining factors in the evolutionary choice of different gradient-sensing strategies of biological cells.

References

[1] Rode et al. https://link.aps.org/doi/10.1103/PRXLife.2.023012

[2] Kromer et al. https://link.aps.org/doi/10.1103/PhysRevLett.124.118101

[3] Jikeli et al. https://www.nature.com/articles/ncomms8985