The AIMS (Automatic Identification and characterisation of Microbial populationS) project is developing and integrating flow cytometric technology for the identification of microbial cell populations and the determination of their cellular characteristics. This involves applying neural network approaches and molecular probes to the identification of cell populations, and deriving and verifying algorithms for assessing the ch emical, optical and morphometric characteristics of these populations. The products of AIMS will be calibrated data, protocols, algorithms and software designed to turn flow cytometric observations into a data matrix of the abundance and cellular characteristics of identifiable populations. This paper describes the general approach of the AIMS project, with details on the application of artificial neural nets and rRNA oligonucleotide probes.
Jonker, R., Groben, R., Tarran, G., Medlin, L., Wilkins, M., García, L., … Boddy, L. (2008). Automated identification and characterisation of microbial populations using flow cytometry: the AIMS project. Scientia Marina, 64(2), 225–234. https://doi.org/10.3989/scimar.2000.64n2225