DOI: 10.1371/journal.pcbi.1014759 ISSN: 1553-7358

From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode

Juhyeon Kim, Hangjun Cho, Jin Hong Mok, Hyeongmin Seo, Joseph Sang-Il Kwon
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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

C a c
and
C l j
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.

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