ArticlePLoS computational biology2012
OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 196 papers, 1 of them a synthesis that pooled it.
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
196 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging genome-scale metabolic models to understand aerobic methanotrophs.The ISME journal · 2024Pooled it
- Systems engineering of engineered live biotherapeutics: A discovery-to-translation framework for streamlining microbiome therapeutic development.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Article
- MOFA: microbial optimization without forced altruism.BMC bioinformatics · 2026Article
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- Water kefir as a paradigm for multi-omics and genome-scale metabolic modelling in fermented food.NPJ biofilms and microbiomes · 2026Review
- Predictive multispecies constraint-based metabolic modeling: case studies and best practices.Briefings in bioinformatics · 2026Review
- Dynamic metabolic modelling of ATP allocation during viral infection.Journal of the Royal Society, Interface · 2026Article
- Personalized Constraint-Based Modeling of Microbial Communities from Metagenomic Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Constraint-Based Metabolic Modeling Approach for Microbial Communities.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Design and Analysis of Macroalgae-Based Biorefineries.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- Leveraging chemical synthesis to discover metabolites from the gut microbiome.Annals of the New York Academy of Sciences · 2025Review
- Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.Bioinformatics (Oxford, England) · 2025Article
- A survey of computational approaches for characterizing microbial interactions in microbial mats.Genome biology · 2025Review
- Model-Guided Rational Construction of Escherichia coli Synthetic Consortia for Enhanced 2-Methylbutyric Acid Production.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- PYF: a multi-functional algorithm for predicting production and optimizing metabolic engineering strategy in Escherichia coli microbial consortia.Briefings in bioinformatics · 2025Article
136 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models.
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.