From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode
5 affiliations
- 1Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, United States of America
- 2Texas A&M Energy Institute, Texas A&M University, College Station, Texas, United States of America
- 3William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, United States of America
- 4Department of Mathematics Education, Kongju National University, Kongju, Republic of Korea
- 5Department of Food Science and Biotechnology, Dongguk University–Seoul, Goyang-si, Gyeonggi-do, Republic of Korea
Abstract
Microbial cocultures exhibit complex population dynamics that are difficult to interpret because internal physiological states are only partially observable. In particular, active and dormant cell states can influence system-level behavior but are rarely resolved from standard fermentation measurements. In this study, we present a hybrid modeling framework that combines structured population-state reconstruction with sparse identification of nonlinear dynamics (SINDy) to analyze a Clostridium acetobutylicum-Clostridium ljungdahlii coculture under perfusion mode. The framework estimates active Cac and Clj biomass-equivalent trajectories from observable biomass and activity measurements, reconstructs dormant populations as model-constrained latent states, and uses these states to identify extracellular metabolite dynamics. After accounting for first-principles perfusion transport, SINDy identified sparse biological reaction terms associated with organic acid turnover, solvent formation, and acetone-to-isopropanol conversion. The resulting model captured active-biomass and metabolite trajectories and suggested that the coculture dynamics are consistent with acid-associated state transitions and sequential metabolic exchange. This framework provides a transparent strategy for interpreting partially observed microbial cocultures while explicitly treating dormant biomass as a latent reconstructed state. Author summary: Synthetic microbial consortia are rapidly emerging as a promising paradigm in modern biotechnology because different organisms can work together to perform complicated tasks. However, elucidating their behavior remains challenging. In particular, important cellular states, such as whether they are actively growing or temporarily inactive, cannot be directly measured. This limits the interpretation of experimental data and the design of effective operating strategies. In this work, we present a modeling approach to reconstruct these hidden aspects of the microbial coculture systems. We first use a mechanistic model to estimate how the total biomass is distributed between active and inactive cells. We then combine this with a data-driven method to identify the key relationships associated with system behavior. Using a mixotrophic Clostridium coculture in continuous perfusion fermentation, we demonstrate that its dynamics are consistent with shifts in cell activity and cooperative interactions between the two species. By mathematically resolving the aggregate biomass into active and dormant subpopulations, we obtained model-supported insights suggesting that the complex dynamics of cocultures arise from a nuanced interplay between stress-induced dormancy and sequential metabolic exchanges, rather than simple growth dynamics. Overall, this approach provides a practical way to better understand and improve various microbial coculture systems.
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BibTeX
@article{kim2026from,
title = {{From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode}},
author = {Juhyeon Kim and Hangjun Cho and Jin Hong Mok and Hyeongmin Seo and Joseph Sang-Il Kwon},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014759},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014759},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode
AU - Juhyeon Kim
AU - Hangjun Cho
AU - Jin Hong Mok
AU - Hyeongmin Seo
AU - Joseph Sang-Il Kwon
PY - 2026
DA - 2026/09/28
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014759
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014759
AB - Microbial cocultures exhibit complex population dynamics that are difficult to interpret because internal physiological states are only partially observable. In particular, active and dormant cell states can influence system-level behavior but are rarely resolved from standard fermentation measurements. In this study, we present a hybrid modeling framework that combines structured population-state reconstruction with sparse identification of nonlinear dynamics (SINDy) to analyze a Clostridium acetobutylicum-Clostridium ljungdahlii coculture under perfusion mode. The framework estimates active Cac and Clj biomass-equivalent trajectories from observable biomass and activity measurements, reconstructs dormant populations as model-constrained latent states, and uses these states to identify extracellular metabolite dynamics. After accounting for first-principles perfusion transport, SINDy identified sparse biological reaction terms associated with organic acid turnover, solvent formation, and acetone-to-isopropanol conversion. The resulting model captured active-biomass and metabolite trajectories and suggested that the coculture dynamics are consistent with acid-associated state transitions and sequential metabolic exchange. This framework provides a transparent strategy for interpreting partially observed microbial cocultures while explicitly treating dormant biomass as a latent reconstructed state. Author summary: Synthetic microbial consortia are rapidly emerging as a promising paradigm in modern biotechnology because different organisms can work together to perform complicated tasks. However, elucidating their behavior remains challenging. In particular, important cellular states, such as whether they are actively growing or temporarily inactive, cannot be directly measured. This limits the interpretation of experimental data and the design of effective operating strategies. In this work, we present a modeling approach to reconstruct these hidden aspects of the microbial coculture systems. We first use a mechanistic model to estimate how the total biomass is distributed between active and inactive cells. We then combine this with a data-driven method to identify the key relationships associated with system behavior. Using a mixotrophic Clostridium coculture in continuous perfusion fermentation, we demonstrate that its dynamics are consistent with shifts in cell activity and cooperative interactions between the two species. By mathematically resolving the aggregate biomass into active and dormant subpopulations, we obtained model-supported insights suggesting that the complex dynamics of cocultures arise from a nuanced interplay between stress-induced dormancy and sequential metabolic exchanges, rather than simple growth dynamics. Overall, this approach provides a practical way to better understand and improve various microbial coculture systems.
SN - 1553-7358
ER -