How far can microbial monocultures predict growth in multi-strain communities?
- 1University of Helsinki
Abstract
Previous research in microbes successfully predicted biculture growth based on monoculture growth curves. Still, the usual model-based approach does not seem to extend to communities involving more than two bacterial strains. Here, we use a model-blind machine-learning approach to predict community-wide yield, area under the growth curve, maximum relative growth rate and its timing in communities involving up to five strains of Escherichia coli. First, we identify the highest per-capita growth rate and its timing in monocultures as major predictors of multi-strain community growth. Next, we show that a random forest trained on communities involving a low number of strains is able to predict the outcomes of communities involving a higher number of strains. Finally, we reveal diminishing returns in using more and more complex communities in order to predict the behaviour of a higher-level community, because monoculture- and biculture-based growth predictions are often already accurate. This finding relativises the need for experiments involving high numbers of strains when studying the growth dynamics of multi-strain communities.
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BibTeX
@misc{borse2026microbial,
title = {{How far can microbial monocultures predict growth in multi-strain communities?}},
author = {F. Borse and S. Lord Smits and T. A. Sun and J. Cairns and A. Farewell and V. Mustonen},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.02.748814},
url = {https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - How far can microbial monocultures predict growth in multi-strain communities?
AU - F. Borse
AU - S. Lord Smits
AU - T. A. Sun
AU - J. Cairns
AU - A. Farewell
AU - V. Mustonen
PY - 2026
DA - 2026/09/02
PB - bioRxiv
DO - 10.64898/2026.09.02.748814
UR - https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1
AB - Previous research in microbes successfully predicted biculture growth based on monoculture growth curves. Still, the usual model-based approach does not seem to extend to communities involving more than two bacterial strains. Here, we use a model-blind machine-learning approach to predict community-wide yield, area under the growth curve, maximum relative growth rate and its timing in communities involving up to five strains of Escherichia coli. First, we identify the highest per-capita growth rate and its timing in monocultures as major predictors of multi-strain community growth. Next, we show that a random forest trained on communities involving a low number of strains is able to predict the outcomes of communities involving a higher number of strains. Finally, we reveal diminishing returns in using more and more complex communities in order to predict the behaviour of a higher-level community, because monoculture- and biculture-based growth predictions are often already accurate. This finding relativises the need for experiments involving high numbers of strains when studying the growth dynamics of multi-strain communities.
ER -