Capability Development and Facilities
The UQ Biosustainability Hub combines expertise with specialised world-class infrastructure, enabling industry-aligned research from early concept through to pilot scale.
We house two NCRIS-funded national research facilities – IDEA Bio and Q-MAP – alongside a purpose-built pre-pilot fermentation facility.
Together, the Biohub facilities alongside leading expertise strengthen Australia's research capability and help drive innovations that are environmentally responsible, economically viable and ready for industry.
Biohub Capability Development and Facilities
Systems metabolic engineering capabilities for synthetic biology within the ARC Centre of Excellence
Chief Investigator(s)
Esteban Marcellin, Lars Nielsen
Background and Need
Synthetic biology increasingly relies on predictive and scalable metabolic‑engineering approaches to design microorganisms capable of producing fuels, chemicals, and biomaterials. However, current strain‑development workflows remain iterative, slow, and insufficiently integrated across modelling, omics analysis, and bioprocess validation. This limits translation of synthetic‑biology discoveries into industrial applications. There is a clear need for integrated systems metabolic‑engineering capabilities that support rational strain design and accelerate development of robust microbial production platforms.
Project Aim
To establish integrated systems metabolic‑engineering capabilities that enable predictive strain design, optimisation, and validation to support synthetic‑biology applications across industrial and environmental sectors.
Approach and Key Activities
The project develops integrated workflows combining genome‑scale metabolic modelling, multi‑omics analysis, and targeted genetic engineering to guide rational strain optimisation. High‑throughput cultivation platforms are used to evaluate engineered strains under defined conditions, while metabolomics and proteomics identify pathway bottlenecks and validate metabolic performance. Iterative design–build–test–learn cycles improve production efficiency and robustness. These capabilities are deployed across multiple synthetic‑biology programs to support development of scalable microbial production systems.
Expected Outcomes
Established systems metabolic‑engineering workflows capable of supporting predictive strain design and optimisation. Additional outputs include validated genome‑scale models, integrated omics datasets, and engineered microbial strains demonstrating improved metabolic performance across selected synthetic‑biology applications.
Impact and Significance
This project strengthens national synthetic‑biology capability by enabling faster and more reliable development of engineered microorganisms. The outcomes support translation of synthetic‑biology research into industrial applications, enhance collaboration across research programs, and position integrated systems metabolic engineering as a core capability underpinning future bio‑based manufacturing in Australia.
Keywords
- Adaptive laboratory evolution
- Bioprocess optimisation
- Bioreactor engineering
- Cell-free biomanufacturing
- Computational biology & AI
- Gas fermentation
- Metabolic modelling
- Multi-omics analysis
- Precision fermentation
- Process scale-up & pilot plant
- Synthetic biology & strain engineering
Q-MAP NCRIS: National Capability for Quantitative Metabolomics, Proteomics and Systems Biology
Chief Investigator(s)
Esteban Marcellin, Lars Nielsen
Background and Need
Modern biotechnology and synthetic biology rely on high‑quality quantitative molecular data to guide strain engineering, bioprocess optimisation, and product development. However, access to advanced metabolomics and proteomics infrastructure remains limited, particularly for researchers and industry seeking scalable and reproducible analytical workflows. This restricts the generation of reliable systems‑level insights needed to accelerate biological innovation. There is a clear need for nationally accessible, integrated mass‑spectrometry capabilities that support research translation and industrial biotechnology development.
Project Aim
To provide nationally accessible quantitative metabolomics and proteomics capabilities that enable systems‑level biological analysis supporting research, industry translation, and biomanufacturing innovation.
Approach and Key Activities
The project operates advanced mass‑spectrometry platforms supporting quantitative metabolomics, proteomics, and targeted analytical workflows. Standardised sample‑preparation, data‑acquisition, and quality‑control protocols ensure reproducibility and comparability across projects. Analytical services support systems‑biology studies, strain‑engineering programs, and bioprocess‑optimisation efforts. Data‑analysis pipelines convert complex molecular datasets into actionable insights, while training and collaborative activities build capability across academic and industry users.
Expected Outcomes
High‑quality metabolomics and proteomics datasets supporting research and industrial biotechnology development. Additional outputs include validated analytical workflows, improved data‑interpretation pipelines, trained personnel, and expanded national access to advanced molecular‑characterisation technologies for diverse biological applications.
Impact and Significance
This project strengthens Australia’s biotechnology capability by providing critical analytical infrastructure that supports innovation across food, health, agriculture, and industrial sectors. The outcomes accelerate translation of biological discoveries into real‑world applications, improve decision‑making in strain and process development, and position Q‑MAP as a national resource enabling advanced systems biology and precision‑fermentation technologies.
Keywords
- Multi-omics analysis
- Other (please specify)
IDEA Bio NCRIS: National Biofoundry Capability for advanced biomanufacturing and synthetic biology
Chief Investigator(s)
Esteban Marcellin, Lars Nielsen
Background and Need
Translation of engineered microbial strains into industrial applications requires reliable bioprocess development and scale‑up capability, yet Australia lacks integrated bioprocess‑biofoundry infrastructure that enables rapid optimisation of fermentation systems. Conventional process‑development workflows remain slow and resource‑intensive, limiting the ability to move promising strains from laboratory discovery to pilot‑scale production. There is a clear need to establish a nationally accessible bioprocess biofoundry that integrates automated fermentation, analytics, and process optimisation to accelerate development of scalable precision‑fermentation technologies.
Project Aim
To establish a nationally accessible bioprocess biofoundry that enables rapid optimisation, scale‑up, and validation of fermentation processes supporting precision and advanced biomanufacturing.
Approach and Key Activities
The project operates automated and instrumented bioreactor platforms supporting parallel fermentation screening and optimisation across multiple scales. Standardised workflows evaluate strain performance, media composition, gas delivery, and operating conditions under controlled environments. Integration with analytical capabilities enables real‑time monitoring of microbial growth, product formation, and metabolic performance. Iterative process‑development cycles generate reproducible datasets supporting scale‑up readiness. Training and collaborative programs promote adoption of biofoundry workflows across academic and industry users.
Expected Outcomes
Validated fermentation workflows capable of supporting rapid strain‑to‑process translation and scale‑up. Additional outputs include optimised bioprocess conditions, reproducible performance datasets, trained personnel, and expanded national access to automated fermentation infrastructure supporting precision‑fermentation development.
Impact and Significance
This project strengthens Australia’s ability to translate biological innovation into industrial‑scale processes by providing a dedicated bioprocess biofoundry for precision fermentation. The outcomes support industry adoption of sustainable biomanufacturing technologies, reduce development timelines, and position IDEA Bio as a national platform enabling scalable production of food, feed, fuels, and biomaterials.
Keywords
- Adaptive laboratory evolution
- Biomaterials & biopolymers
- Bioprocess optimisation
- Bioreactor engineering
- Cell-free biomanufacturing
- Computational biology & AI
- Environmental engineering
- Gas fermentation
- Metabolic modelling
- Microbiome engineering & biodiscovery
- Multi-omics analysis
- Precision fermentation
- Process scale-up & pilot plant
- Resource recovery (e.g. biomining)
- Synthetic biology & strain engineering
- Waste valorisation
- Wastewater treatment
ARC COESB C1 Atlas
Chief Investigator(s)
Background and Need
C1 fermentation offers major potential for converting waste gases into fuels, food, and fibre. Although many natural and synthetic routes for C1 capture have been explored, there is still no reliable strategy for co‑utilising methane and carbon dioxide — the most theoretically and economically advantageous approach. Existing pathway‑prediction algorithms typically handle only single substrates, limiting their ability to address this multi‑substrate challenge. A new computational strategy is needed to discover feasible, novel biochemical pathways capable of simultaneous CH₄ and CO₂ conversion.
Project Aim
To develop new computational methods for discovering multi‑substrate C1‑capture pathways, including simultaneous methane and carbon‑dioxide utilisation.
Approach and Key Activities
The project is developing a breadth‑first search algorithm enhanced with new heuristics to expand and refine the biochemical solution space. Novel reactions consistent with biochemical rules — but not yet observed in nature — are incorporated to uncover previously unexplored pathways. Thermodynamic and discovery‑based filters are applied to constrain the search and prevent combinatorial explosion, particularly in long or cyclic pathways. This framework will be applied to identify viable routes for co‑utilising methane and carbon dioxide.
Expected Outcomes
A new reaction database in RInChI format suitable for AI applications; software for exploring C1‑capture pathways and other complex multi‑substrate networks; and specific pathway designs enabling simultaneous methane and carbon‑dioxide capture.
Impact and Significance
This project advances computational pathway discovery and supports the development of next‑generation C1‑fermentation technologies. It enables exploration of novel biochemical routes with industrial relevance, strengthens Australia’s synthetic‑biology and modelling capabilities, and contributes to sustainable carbon‑recycling strategies.
Keywords
- Computational biology & AI
- Metabolic modelling
Development of untargeted lipidomics services at Q-MAP
Chief Investigator(s)
Shana John / Q-MAP
Background and Need
Q‑MAP currently provides a targeted lipidomics service optimised for mammalian lipids, but the coverage is insufficient for the far more diverse lipid species found in microbial organisms. Microbial lipids play critical roles in bioengineering, sustainable chemistry, and synthetic biology, yet remain under‑characterised due to analytical limitations. There is a clear need for an untargeted, global lipidomics workflow capable of capturing the full breadth of microbial lipid diversity to support research across the BioHub.
Project Aim
To develop and validate an untargeted global lipidomics technique using liquid chromatography–high‑resolution mass spectrometry (LC‑HRMS) for comprehensive analysis of microbial lipids.
Approach and Key Activities
The project will establish robust sample‑preparation protocols for comprehensive extraction of microbial lipids, optimise LC‑HRMS acquisition settings to detect diverse lipid classes, and evaluate annotation confidence across detected species. Comparative analyses will be performed across multiple microbial organisms to assess method robustness and biological relevance. The resulting workflow will expand Q‑MAP’s analytical capabilities into a metabolic space that is rarely studied but highly valuable for biomanufacturing and synthetic‑biology applications.
Expected Outcomes
A validated, robust untargeted lipidomics platform capable of analysing diverse microbial lipidomes. Additional outputs include optimised extraction protocols, LC‑HRMS methods, lipid‑annotation confidence metrics, and datasets supporting BioHub projects in biofuels, fatty‑acid production, and membrane‑engineering research.
Impact and Significance
This new analytical capability will significantly expand Q‑MAP’s metabolomics portfolio, enabling researchers to explore previously inaccessible metabolic pathways. It will support multiple BioHub programs, enhance understanding of microbial lipid biology, and accelerate innovation in sustainable chemistry, biofuel development, and engineered‑cell‑factory design.
Keywords
- Metabolic modelling
- Multi-omics analysis
Development of phosphoproteomics analytical workflow enabling research projects required a platform for fast and deep phosphoproteome analysis
Chief Investigator(s)
Anh Phan, Shana John, Craig Barry, Esteban Marcellin
Background and Need
Phosphoproteomics is an essential analytical workflow for understanding protein‑phosphorylation events and their biological functions across the proteome. It enables investigation of cellular‑signalling pathways that regulate growth, division, communication, and complex biological processes. Q‑MAP has recently seen strong demand for a reliable phosphorylation‑focused analytical solution, highlighting the need for a robust, high‑quality workflow to support research across the Biosustainability Hub and the broader UQ community.
Project Aim
To develop a fast, robust, and deep phosphoproteomics analytical workflow at the Q‑MAP core facility.
Approach and Key Activities
The project will optimise phosphopeptide‑enrichment methods reported in the literature — including EasyPhos, MagReSyn® Ti‑IMAC HP, and commercial TiO₂ spin tips — to enable detection of low‑abundance phosphopeptides and dynamic phosphorylation changes under varying biological conditions. In parallel, data‑independent acquisition (DIA) methods will be developed and refined on the Orbitrap Astral mass spectrometer to achieve rapid, deep phosphoproteome coverage. This integrated approach will support high‑throughput identification and quantification of phosphorylation sites across diverse sample types.
Expected Outcomes
A validated phosphoproteomics workflow capable of supporting academic research needs and providing analytical solutions for industry applications.
Impact and Significance
This workflow will be a valuable addition to the Q‑MAP research platform, directly supporting the Biosustainability Hub, the Centre of Excellence in Synthetic Biology, the wider UQ research community, and industry partners. It expands Q‑MAP’s capability to deliver high‑resolution molecular insights essential for modern bioscience and biomanufacturing.
Keywords
- Bioprocess optimisation
- Microbiome engineering & biodiscovery
- Multi-omics analysis
- Other (please specify)
Smart bioprocess development: from lab bench to industrial scale
Chief Investigator(s)
Tim McCubbin, Esteban Marcellin, Axayacatl Gonzalez
Background and Need
A major challenge for precision‑fermentation companies is scale‑up, as processes optimised at laboratory scale frequently fail under pilot or commercial conditions. Differences in mixing, mass transfer, shear stress, and nutrient gradients are poorly captured by existing small‑scale experiments. These limitations slow industrial deployment, increase development costs, and hinder delivery of sustainable biomanufactured products with environmental and societal benefits.
Project Aim
To develop a digital‑twin platform that enables laboratory‑scale bioreactors to accurately mimic pilot‑scale fermentation conditions.
Approach and Key Activities
The project will develop a software platform integrating computational fluid dynamics with machine learning to model large‑scale bioreactor environments. These models will be linked to TJX Bioengineering’s automated laboratory reactors, enabling real‑time physical control that reproduces industrial‑scale gradients and stresses at lab scale. Experimental data will iteratively refine the digital twin, while AI‑driven optimisation identifies robust operating strategies. This data‑driven approach directly connects modelling, experimentation, and process optimisation across scales.
Expected Outcomes
A validated digital twin of industrial bioprocesses, software for controlling lab‑scale reactors to emulate large‑scale conditions, and AI‑guided optimisation tools. Additional outputs include improved scale‑up protocols, high‑quality process datasets, and demonstration case studies relevant to precision‑fermentation workflows.
Impact and Significance
This project will benefit precision‑fermentation companies by reducing scale‑up risk, development time, and cost. Improved predictability accelerates commercialisation of sustainable bio‑based products, supporting environmental goals and industrial competitiveness. The platform also provides a transferable tool for broader biomanufacturing applications, strengthening Australia’s advanced‑manufacturing and biotechnology capabilities.
Keywords
- Bioprocess optimisation
- Computational biology & AI
- Metabolic modelling
- Multi-omics analysis
- Precision fermentation
- Process scale-up & pilot plant
Metabolic flux modelling to enhance co-utilisation of biomass sugars for high-yield hydrogen biocatalysts
Chief Investigator(s)
Timothy McCubbin, Justin Chitpin, Axayacatl Gonzalez
Background and Need
Efficient conversion of mixed sugar streams (glucose, xylose, arabinose) into hydrogen remains a major challenge for sustainable biomanufacturing. Although engineered strains can co‑utilise these substrates, scale‑dependent effects alter metabolic behaviour, reducing yields and process predictability. Current approaches lack detailed, quantitative understanding of carbon and electron flux under industrial conditions. There is a critical need for integrated modelling and experimental frameworks to identify bottlenecks and enable high‑yield, scalable hydrogen production.
Project Aim
To quantify and optimise carbon and electron fluxes in a multi‑sugar‑utilising biocatalyst to improve hydrogen yield and enable robust, scalable performance in continuous bioreactor systems.
Approach and Key Activities
The project integrates experimental and computational approaches to map and optimise metabolic fluxes. ¹³C Metabolic Flux Analysis (MFA) will quantify intracellular flux distributions under varying media and scale conditions, supported by metabolomics, off‑gas analysis, and proteomics. Flux Balance Analysis (FBA) will model steady‑state behaviour in continuous‑flow reactors using constraints derived from MFA and extracellular balances. Additional analyses will identify and quantify carbon diversion into atypical metabolic pathways, improving model accuracy. Together, these approaches will guide strain and process optimisation for enhanced hydrogen production.
Expected Outcomes
Quantitative flux maps linking substrate utilisation to hydrogen production; validated MFA and FBA models; and identification of metabolic bottlenecks. Additional outputs include improved analytical methods for atypical metabolites and defined engineering targets for strain improvement and process optimisation, supporting scale‑up and commercial deployment.
Impact and Significance
This project advances sustainable hydrogen production by improving microbial conversion efficiency of mixed sugars, including lignocellulosic feedstocks. It supports Australia’s clean‑energy and biomanufacturing goals by enabling scalable, high‑yield processes. Broader impacts include strengthening modelling and systems‑biology capabilities, accelerating industrial translation, and positioning Australia as a leader in bio‑based hydrogen‑production technologies.
Keywords
- Computational biology & AI
- Metabolic modelling
- Multi-omics analysis