HW Week 1: Final Project Idea
ABC’s: Algal-Bacterial Consortium for Growing Aromatic and Biofuel Compounds from Lignin Waste
Author: Matthew Javier Gaughan
Abstract
This project will grow a metabolically optimized microalgal and bacterial consortia to produce both value-added chemicals and lipid-rich biomass flocks for biofuels production from phenolic-rich wastewater content1–5. Phenolic-rich waste feedstocks occur during agricultural production, biomass refining, and industrial chemical runoff, creating an environmental pollutant. This makes addressing the challenge of remediating and converting phenolic-rich waste streams, such as those from lignin refining, an imperative6,7. Certain microalgae species are documented playing a role in phenolic and nitrate remediation from wastewater streams and can metabolize and produce aromatic platform compounds2,4,5,8–10. This presents a potential route for monolignol upgrading from depolymerized lignin waste, while simultaneously operating as a carbon sink and local source for sustainable fuel5,6. Within the timescale of the HTBAA course, this project will deliver a locally sourced microalgae growth on a phenol-enriched media for future metagenomic and metabolomic testing.
Part 1: Target Compounds
This process yields two primary bio-manufacturing products. First, microalgae contain high lipid content by mass, providing a source of biofuel precursors (biodiesel, bioethanol, biomethane) 2,8,11,12. Any microalgal growth would necessarily convert feedstock into lipid content, dependent on nitrogen content within the feed8,9. Thus, the first target product is a bulk, lipid-enriched biomass for conversion to biofuels. Biofuel production from waste feedstock is viewed as a supply-chain stopgap for instances where imported fuel cannot be obtained, such as during natural disasters, but could also potentially be scaled through metabolic optimization to serve as a supplementary or primary fuel source for local grids12.
The second bio-manufacturing target of this process would be a platform aromatic species. A potential source of phenolic rich waste streams is lignocellulosic biomass waste5,9,10,13. Depolymerized lignin is rich in aromatic phenolic monomers, but requires funneling and upgrading for a scaled, target, platform aromatic production process5,14. Microalgae can convert phenols to catechol, which in turn serves as a platform aromatic for compounds applied in fragrance, flavor, and pharmaceuticals production4,13. Paired with a consortia of aromatic-funneling bacteria, this process would aim to optimize selective aromatic production3,14. In this initial proposal, the target aromatic should be catechol, but an alternative bioactive compound could be proposed, optimized with a different bacterial consortium for additional funneling.
Part 2: Microbial Chassis
The living system employed in this project is referred to as a microbial consortium1,3. It would consist of a selection of microalgal and bacterial species whose symbiosis would function to produce biomass flocks enriched in target aromatics and lipid content. Consortium biomass flocks would be grown within a liquid nutrient feed composed of waste stream from lignocellulosic biomass, which would have been pretreated to break down lignin into a phenolic-rich feedstock. Algal-bacterial flocks would be grown within bioreactors on a this wastewater media supplemented with nutrients for optimal growth1,4,5.
Microalgae refers to single-cell species of algae, which maintain the photosynthetic metabolic routes of the broader algae group. Recent advancements in synthetic biology have made microalgae a promising platform species for metabolic engineering11,15. Due to their photosynthetic metabolism, microalgae require fewer nutrient inputs for optimizing bioprocess development, playing a critical role in scaling biomanufacturing and improving overall process sustainability by optimizing utilization of waste feedstocks2,5,8,11.
Microalgae would produce high-lipid content while remediating phenols found in the feed slurry. Supplementary funneling and upgrading of monolignols into target aromatics would occur through metabolic pathways present in the selected bacteria. The microalgae-bacteria pairing would be done to optimize for aromatics conversion and lipid production. Microalgae have been shown to fully remediate phenols present in wastewater, thus serving to improve lignin conversion, while the symbiosis of microalgae and bacteria could improve target yield and selectivity. As part of this project, metabolic optimization in the form of knockouts and knock-ins could improve carbon flux into specific target compounds through overexpressing enzymes involved in deoxygenation, decarboxylation, or reaction cofactors14,16.
Microalgae sequester carbon dioxide through photosynthesis. This process presents several advantages for reducing costly nutrient inputs typical to bioprocess development. Additionally, microalgae carbon dioxide sequestration should further reduce the carbon footprint of the process. Finally, microbial consortia have been shown to improve bioreactor performance related to substrate conversion and target yields. Optimizing a consortium for production of biofuels and target aromatics would potentially increase titers for each target product. At the scale of the HTBAA course, consortia development would consist of achieving a locally sourced microalgae growth on simulated lignin-derived wastewater media and preparing the growth as samples for metagenomic and metabolomic analysis.
Part 3: The Role of Automation
Automation would play a role in culturing, sampling, and measurement. Bioreactor regulation would necessitate automation to ensure optimal temperature and pH conditions throughout the target incubation period. This monitoring system would likely consist of liquid-handling robots as well as microfluidics platforms for applying metagenomic kits and metabolomic assays on consortia samples. Analytical testing could be automated but would occur downstream of the reactor.
Machine learning could play a role informing regulation based on measurement data related to metagenomic and metabolomic expression within the consortia16-18. Gene expression correlated with target product optimization could be monitored and conditions tuned in response to the development of new optimized strains over time.
Part 4: Timeline
As the immediate role of algal biofuel production is for stopgap emergency fuel production in disrupted supply chains, scale would aim to accommodate the fuel needs of a hospital grid. A single unit of production (with multiple bioreactors) should aim to achieve this fuel volume (~3400 gallons). Platform aromatics production scale would also be scaled down from industrial production to achieve the more immediate role as a supply chain stopgap.
The bioreactor should aim to achieve peak titer within a one-week timeline, ideally employing a staggered growth schedule across multiple reactors to ensure continual fuel and aromatics production. Separation and purification processes would be anticipated to take the shortest amount of time relative to inoculation and growth.
The development of a functional bioreactor system would likely take multiple years with the initial phase of the project focused on consortia selection and metabolic optimization, a challenging task for multiple species across several genera. This project aims to focus specifically on consortia selection and identifying metabolic routes for engineering. These initial research steps could likely be done within three months prior to proceeding to testing the growth conditions of the consortia. The bulk of the timeline would focus on optimizing the growth conditions after target chemical products were identified as a proof-of-concept. At the bench scale, the product titer would aim to achieve multiple grams worth of aromatic and lipid products.
Part 5: Challenges and Barriers
The largest technological unknown is the viability of microalgal-bacterial consortia. Symbiosis has been applied in prior research, but optimization towards a target product requires additional research. Research on the application of microbial consortia for bioprocessing is nascent, but literature has shown a potentially synergistic impact on target product titer and feedstock conversion. Inherent to the development of a consortium are the challenges of optimizing a complex, multi-species, multi-product system. Modelling multi-metabolic systems remains a challenge. Additionally, focusing on two target end-products necessitates the development of multiple separation processes which make the project technically involved. Roadblocks would be expected as optimizing singular species and singular products is already challenging.
Unique to this project is the exploration of microalgae as a chassis for phenols conversion to target platform aromatics. Microalgae are known to remediate phenols from waste streams, but little characterization work has classified the aromatic end products present in microalgae due to the inherent complexity of quantifying phenols in algal systems. Additionally, this project would produce data related to consortia development and optimization which remains underexplored.
Creating a fully optimized process would occur over a multi-year timespan starting from initial strains, which remains unfeasible for the timeline of this course. Instead, within the timespan of HTBAA, the project will focus on cultivating local microalgae strains on lignin wastewater. A simulated, phenol-rich nutrient media will serve as the substrate for microalgae growth and sequencing of local algae metagenomes will provide a starting point for consortia selection. Finally, bench-scale growths will be characterized in terms of lipid, protein, and phenolic (initial phenolic target: catechol) content.
Works Cited
(1) Dou, X.-X.; Mao, B.-D.; Li, A.; Gu, J.-J.; Zhang, X.-L.; Fu, C.-W.; Zhang, X.-J.; Lan, B.-J.; Xu, J.-W.; Zhang, B.-X.; Zheng, H.-J.; Gao, F. Algal-Bacterial Symbiosis Strengthens the Treatment of High-Salinity Phenolic Wastewater and Its Molecular Mechanism. J. Hazard. Mater. 2025, 498, 139957. https://doi.org/10.1016/j.jhazmat.2025.139957
(2) Deshmukh, M.; Kothawade, T. R.; Pathan, A.; Behera, U. S.; Sangwai, J. S.; Byun, H.-S. A Review on Sustainable Microalgae-Based Approaches for CO2 Capture, Bioenergy, and Value-Added Products. Korean J. Chem. Eng. 2026, 43 (5), 1277–1314. https://doi.org/10.1007/s11814-025-00632-9
(3) Troiano, D. T.; Studer, M. H.-P. Microbial Consortia for the Conversion of Biomass into Fuels and Chemicals. Nat. Commun. 2025, 16 (1), 6712. https://doi.org/10.1038/s41467-025-61957-x
(4) Zou, L.-G.; Yao, Y.-T.; Wen, F.-F.; Zhang, X.; Liu, B.-T.; Li, D.-W.; Yang, Y.-F.; Yang, W.-D.; Balamurugan, S.; Li, H.-Y. Bioremediation of Catechol and Concurrent Accumulation of Biocompounds by the Microalga Crypthecodinium Cohnii. J. Agric. Food Chem. 2023, 71 (26), 10065–10074. https://doi.org/10.1021/acs.jafc.3c01814
(5) Wang, H.; Qin, L.; Qi, W.; Elshobary, M.; Wang, W.; Feng, P.; Wang, Z.; Zhu, S. Harmony in Detoxification: Microalgae Unleashing the Potential of Lignocellulosic Pretreatment Wastewater for Resource Utilization. Sci. Total Environ. 2024, 927, 171888. https://doi.org/10.1016/j.scitotenv.2024.171888
(6) Mateo, S.; Fabbrizi, G.; Moya, A. J. Lignin from Plant-Based Agro-Industrial Biowastes: From Extraction to Sustainable Applications. Polym. Basel 2025, 17 (7), 952.
(7) Meraj, A.; Singh, S. P.; Jawaid, M.; Nasef, M. M.; Alomar, T. S.; AlMasoud, N. A Review on Eco-Friendly Isolation of Lignin by Natural Deep Eutectic Solvents from Agricultural Wastes. J. Polym. Environ. 2023, 31 (8), 3283–3316. https://doi.org/10.1007/s10924-023-02817-x
(8) Chu, R.; Li, S.; Zhu, L.; Yin, Z.; Hu, D.; Liu, C.; Mo, F. A Review on Co-Cultivation of Microalgae with Filamentous Fungi: Efficient Harvesting, Wastewater Treatment and Biofuel Production. Renew. Sustain. Energy Rev. 2021, 139, 110689. https://doi.org/10.1016/j.rser.2020.110689
(9) Zhu, J.; Tan, X.; Saadiah Hafid, H.; Wakisaka, M. A Novel Strategy to Promote Microalgal Growth and Lipid Productivity by Supplementation of Lignin Related Phenolic Elicitors. Fuel 2023, 334, 126775. https://doi.org/10.1016/j.fuel.2022.126775
(10) Lindner, A. V.; Pleissner, D. Utilization of Phenolic Compounds by Microalgae. Algal Res. 2019, 42, 101602. https://doi.org/10.1016/j.algal.2019.101602
(11) Sharma, P. K.; Saharia, M.; Srivstava, R.; Kumar, S.; Sahoo, L. Tailoring Microalgae for Efficient Biofuel Production. Front. Mar. Sci. 2018, 5, 382. https://doi.org/10.3389/fmars.2018.00382
(12) Biofuels as an interim solution for hard-to-electrify sectors | Columbia Business School. https://business.columbia.edu/insights/climate/biofuels-interim-solution-hard-electrify-sectors (accessed 2026-09-22)
(13) Rastrojo-Velasco, J. M.; Leon, R.; Sayago, A.; Fernandez-Recamales, A.; Vigara, J.; Leon-Vaz, A. Effect Produced by a Mixture of Phenol, p-Cresol, and Acetophenone on Four Species of Microalgae: Tolerance, Biodegradation, and Metabolic Alterations. Toxics 2025, 13 (10), 848. https://doi.org/10.3390/toxics13100848
(14) Erickson, E.; Bleem, A.; Kuatsjah, E.; Werner, A. Z.; DuBois, J. L.; McGeehan, J. E.; Eltis, L. D.; Beckham, G. T. Critical Enzyme Reactions in Aromatic Catabolism for Microbial Lignin Conversion. Nat. Catal. 2022, 5 (2), 86–98. https://doi.org/10.1038/s41929-022-00747-w
(15) Stavridou, E.; Karapetsi, L.; Nteve, G. M.; Tsintzou, G.; Chatzikonstantinou, M.; Tsaousi, M.; Martinez, A.; Flores, P.; Merino, M.; Dobrovic, L.; Mullor, J. L.; Martens, S.; Cerasino, L.; Salmaso, N.; Osathanunkul, M.; Labrou, N. E.; Madesis, P. Landscape of Microalgae Omics and Metabolic Engineering Research for Strain Improvement: An Overview. Aquaculture 2024, 587, 740803. https://doi.org/10.1016/j.aquaculture.2024.740803
(16) Liu, R.-Y.; Li, B.-Z.; Yuan, Y.-J.; Liu, Z.-H. Multiscale Metabolic Engineering in Biological Lignin Valorization. The Innovation 2025, 6 (11), 100993. https://doi.org/10.1016/j.xinn.2025.100993
(17) Bai, L.; You, Q.; Zhang, C.; Sun, J.; Liu, L.; Lu, H.; Chen, Q. Advances and Applications of Machine Learning and Intelligent Optimization Algorithms in Genome-Scale Metabolic Network Models. Syst. Microbiol. Biomanufacturing 2023, 3 (2), 193–206. https://doi.org/10.1007/s43393-022-00115-6
(18) Malli, A.; Vasyutyn, D.; Kim, J. R. Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters. J. Chem. Inf. Model. 2026, 66 (1), 42–60. https://doi.org/10.1021/acs.jcim.5c02428