2026-10-11T21:01:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34017905 2026-09-28T17:57:40Z category_4 category_7 category_21 category_272 category_873 category_39 category_734 category_811 portal_5 item_type_1 month_year_09_2026

<p>Overview of the proposed modeling framework.</p> Juhyeon Kim (3796651) Hangjun Cho (24418434) Jin Hong Mok (25132877) Hyeongmin Seo (7504133) Joseph Sang-Il Kwon (6894362) Biochemistry Medicine Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified sequential metabolic exchange reconstructs dormant populations div >< p combines structured population internal physiological states hidden cell states dormant cell states constrained latent states standard fermentation measurements associated state transitions principles perfusion transport observable fermentation data clostridium ljungdahlii </ clostridium acetobutylicum </ hybrid modeling framework organic acid turnover framework estimates active clostridium </ framework provides state reconstruction activity measurements perfusion mode partially observable transparent strategy sparse identification solvent formation rarely resolved nonlinear dynamics modeling study metabolite trajectories level behavior isopropanol conversion influence system equivalent trajectories <div><p>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 <i>Clostridium acetobutylicum</i>-<i>Clostridium ljungdahlii</i> coculture under perfusion mode. The framework estimates active and 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.</p></div> 2026-09-28T17:57:36Z Image Figure 10.1371/journal.pcbi.1014759.g001 https://figshare.com/articles/figure/_p_Overview_of_the_proposed_modeling_framework_p_/34017905 CC BY 4.0