Microalgal co-cultivation -recent methods, trends in omic-studies, applications, and future challenges
Bibliographic record
Abstract
The burgeoning human population has resulted in an augmented demand for raw materials and energy sources, which in turn has led to a deleterious environmental impact marked by elevated greenhouse gas (GHG) emissions, acidification of water bodies, and escalating global temperatures. Therefore, it is imperative that modern society develop sustainable technologies to avert future environmental degradation and generate alternative bioproduct-producing technologies. A promising approach to tackling this challenge involves utilizing natural microbial consortia or designing synthetic communities of microorganisms as a foundation to develop diverse and sustainable applications for bioproduct production, wastewater treatment, GHG emission reduction, energy crisis alleviation, and soil fertility enhancement. Microalgae, which are photosynthetic microorganisms that inhabit aquatic environments and exhibit a high capacity for CO2 fixation, are particularly appealing in this context. They can convert light energy and atmospheric CO2 or industrial flue gases into valuable biomass and organic chemicals, thereby contributing to GHG emission reduction. To date, most microalgae cultivation studies have focused on monoculture systems. However, maintaining a microalgae monoculture system can be challenging due to contamination by other microorganisms (e.g., yeasts, fungi, bacteria, and other microalgae species), which can lead to low productivity, culture collapse, and low-quality biomass. Co-culture systems, which produce robust microorganism consortia or communities, present a compelling strategy for addressing contamination problems. In recent years, research and development of innovative co-cultivation techniques have substantially increased. Nevertheless, many microalgae co-culturing technologies remain in the developmental phase and have yet to be scaled and commercialized. Accordingly, this review presents a thorough literature review of research conducted in the last few decades, exploring the advantages and disadvantages of microalgae co-cultivation systems that involve microalgae-bacteria, microalgae-fungi, and microalgae-microalgae/algae systems. The manuscript also addresses diverse uses of co-culture systems, and growing methods, and includes one of the most exciting research areas in co-culturing systems, which are omic studies that elucidate different interaction mechanisms among microbial communities. Finally, the manuscript discusses the economic viability, future challenges, and prospects of microalgal co-cultivation methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".