ArticleBiotechnology for biofuels and bioproducts2026
Integrative multi-omic and phenotypic analysis of open raceway pond production of Monoraphidium minutum 26B-AM reveals distinct stress signatures for scale-up and infection.
Article in Biotechnology for biofuels and bioproducts, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
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Authors and funding
10 authors.
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Abstract
backgroundGreen microalgae, such as Monoraphidium minutum 26B-AM, have garnered significant commercial interest due to their high biomass production and lipid yield, providing promising candidates for various bioprocessing applications. However, the economic viability of large-scale algal cultivation in open raceway ponds is limited by biocontamination and environmental stressors, necessitating deeper understanding of the molecular mechanisms that underpin resilience and productivity in these systems. We hypothesized that the molecular signature associated with the cellular responses of M. minutum to environmental stressors will reveal critical information for the timely prediction of resilience and productivity in algal cultures within open pond systems.
resultsTo test this hypothesis, we conducted a longitudinal multi-omic study, integrating transcriptomics, proteomics, metabolomics, and phenomics, to monitor the acclimation, growth dynamics, and pathogen responses of algal cultures in two 1000 L raceway ponds, before and after the introduction of a pathogen as a stressor. We identified a number of molecular patterns that correlate with changes in the algal environment, and we can track these changes within the ponds per time. Furthermore, we identify scale-up and infection-specific molecular pathways through integrated multi-omics, showing that most patterns are unique to each studied stressor/transition.
conclusionsUltimately, this study demonstrates the utility of multi-omics observations at scale, revealing unique signatures and laying the groundwork for developing molecular detection techniques and predictive models that can improve the sustainability and efficiency of large-scale algae biomass production.
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