Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Single cells can be characterized in terms of their phenotypic properties using flow cytometry. However, up to our knowledge there has not yet been a thorough survey concerning the classification of bacterial species based on flow cytometric data. This paper aims to perform a thorough investigation concerning the identification of bacterial communities of various complexities in species richness. We do this by creating so-called in silico communities, communities created by aggregating the data coming from individual cultures; moreover we show that it is possible to use in silico communities to identify in vitro created communities as well, proving the biological relevance and usability of bacterial in silico communities