ArticlePLoS biology2019
Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells.
Article in PLoS biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Identification of dynamic models of microbial communities: A workflow addressing identifiability and modeling pitfalls.PLoS computational biology · 2025Article
- Metabolic interplay drives population cycles in a cross-feeding microbial community.Nature communications · 2025Article
- Competition and cooperation: The plasticity of bacterial interactions across environments.PLoS computational biology · 2025Article
- Advances in synthetic microbial ecosystems approach for studying ecological interactions and their influencing factors.Engineering microbiology · 2025Review
- Article
- Exploring interspecific interaction variability in microbiota: A review.Engineering microbiology · 2024Review
- Resource competition predicts assembly of gut bacterial communities in vitro.Nature microbiology · 2024Article
- Model-free prediction of microbiome compositions.Microbiome · 2024Article
- Bacterial adenine cross-feeding stems from a purine salvage bottleneck.The ISME journal · 2024Article
- Collective production of hydrogen sulfide gas enables budding yeast lacking MET17 to overcome their metabolic defect.PLoS biology · 2023Article
- Article
- Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics.eLife · 2022Article
- ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks.GigaScience · 2022Article
- Article
- Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models.Current opinion in microbiology · 2021Review
- Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure.Journal of molecular evolution · 2021Article
- Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales.Nature communications · 2021Article
- Article
- Combining SIMS and mechanistic modelling to reveal nutrient kinetics in an algal-bacterial mutualism.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Quantitative modeling is useful for predicting behaviors of a system and for rationally constructing or modifying the system. The predictive power of a model relies on accurate quantification of model parameters. Here, we illustrate challenges in parameter quantification and offer means to overcome these challenges, using a case example in which we quantitatively predict the growth rate of a cooperative community. Specifically, the community consists of two Saccharomyces cerevisiae strains, each engineered to release a metabolite required and consumed by its partner. The initial model, employing parameters measured in batch monocultures with zero or excess metabolite, failed to quantitatively predict experimental results. To resolve the model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then remeasured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured, including death rate, metabolite release rate, and the amount of metabolite consumed per cell birth, varied significantly with the metabolite environment. Once we used parameters measured in a range of community-like chemostat environments, prediction quantitatively agreed with experimental results. In summary, using a simplified community, we uncovered and devised means to resolve modeling challenges that are likely general to living systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.