Supporting Information and Data for Machine Learning Algorithms Applied to Identify Microbial Species by their Motility

Abstract

Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potentially alien life forms, for which ‘motility’ is an excellent candidate. Here we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates of the selected species does not exceed 82%. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.