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

doi:10.1371/journal.pcbi.1014759

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

Journal article

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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 and Joseph Sang-Il Kwon

PLoS computational biology, Vol.22(9), e1014759

09/2026

DOI: 10.1371/journal.pcbi.1014759

PMCID: PMC13619069

PMID: 42804546

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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.

Computational Biology

2-Propanol - metabolism

Acetone - metabolism

Biomass

Clostridium - metabolism

Clostridium - physiology

Clostridium acetobutylicum - metabolism

Clostridium acetobutylicum - physiology

Coculture Techniques - methods

Computer Simulation

Fermentation - physiology

Models, Biological

Details

Title: Subtitle: From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode
Creators: Juhyeon Kim - The Ohio State University
Hangjun Cho - Kongju National University
Jin Hong Mok - Dongguk University
Hyeongmin Seo - University of Iowa
Joseph Sang-Il Kwon - The Ohio State University
Resource Type: Journal article
Publication Details: PLoS computational biology, Vol.22(9), e1014759
DOI: 10.1371/journal.pcbi.1014759
PMID: 42804546
PMCID: PMC13619069
ISSN: 1553-7358
eISSN: 1553-7358
Publisher: PUBLIC LIBRARY SCIENCE
Grant note: United States Department of Agriculture (USDA) - National Institute of Food and Agriculture (NIFA).: 2025-38502-45540 National Science Foundation: NSF-2435227
This research is partially supported by the National Science Foundation Global Center program (NSF-2435227 to HS), the NSF EPSCoR RII Track-1 program (NSF-2242763 to HS), and the USDA-NIFA Sun Grant program (2025-38502-45540 to HS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Financial support from the William G. Lowrie Department of Chemical and Biomolecular Engineering at The Ohio State University is gratefully acknowledged.
Language: English
Date published: 09/2026
Academic Unit: Chemical and Biochemical Engineering
Record Identifier: 9985237506202771

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