Doctoral Candidates

Project title: Advanced Strategies for Smart Biomanufacturing in Biofoundries

Manuel's project focuses on improving the robustness of yeast strains for biomanufacturing high-value chemicals from regionally relevant second generation lignocellulosic biomass. Phenolic byproducts from the pretreatment act as inhibitors for fermentation, but are also intermediary nodes for high value phenolic compounds, such as resveratrol, naringenin and rosmarinic acid. The project uses computational tools to streamline strain design, including retrobiosynthesis pathway design, enzyme prioritization, transcription factor identification for biosensor design, and metabolic modeling to identify engineering targets. These are combined with medium and high-throughput experimental methods (stress-responsive biosensors, enzymatic assays and quantification of stress biomarkers) for characterization of inhibition and stress responses in both real feedstocks and synthetic analogs. This data will be used both for identification of engineering targets for an improved stress response and to refine genome scale metabolic models of stress. Feedstock and pathway associated stress effects will be evaluated at multiple scales, including deep-well plates, pioreactors and lab-scale bioreactors.

Background fields: biochemistry, metabolic engineering, synthetic biology, lab automation

Project title: Biosensors and Dynamic Regulation for Robust Biomanufacturing

Matteo's project focuses on the development and modelling of biosensors and dynamic regulatory systems for robust biomanufacturing. The project aims to develop mRNA- and DNA-based biosensors capable of monitoring cellular states and metabolic activity in engineered cell factories, and to use this information to enable dynamic regulation of metabolic pathways. A hybrid modelling approach combining mechanistic and data-driven methods will be used to understand, predict and design molecular circuits and biosensors, with the ultimate goal of integrating sensing and control strategies into biomanufacturing processes. By enabling more responsive and adaptive regulation of engineered pathways, the project seeks to improve the stability, robustness and efficiency of microbial cell factories.

Background fields: physics of complex systems, statistical physics, computational modeling, numerical simulations

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Project title: Fermentation and DSP technologies to produce non-conventional SCP from sustainable feedstocks

Within FrameBio, the PhD project aims to assess non-conventional microbial hosts for single-cell protein (SCP) production, in terms of biomass yield, nutritional quality, downstream processing (DSP) requirements, and overall sustainability. The project will characterize different sustainable feedstocks and assess their fermentability through liquid and gas fermentation strategies, with the aim of producing high-density, high-protein biomass. Downstream, it will investigate protein recovery, digestibility, functional properties, and processability, generating data that will feed into techno-economic and life cycle assessments (TEA/LCA) of the resulting production routes. The project will guide the selection of sustainable feedstock–host–DSP combinations for scalable, industrially relevant SCP production. 

Background fields: biotechnology, bioprocess engineering, fermentation, downstream processing, techno-economic analysis, process optimization

Project title: Data-driven production process enumeration framework for SCP production, to benchmark economic, environmental and socio-economic performance

Christina will develop a data-driven framework to enumerate and evaluate different production process routes for single-cell protein (SCP), benchmarking their economic, environmental, and socio-economic performance across production schemes such as solid-state, liquid-state, and gas-state fermentation. Using tools including BioSTEAM, Brightway, she will systematically map the SCP production design space and identify pathways that are both economically viable and environmentally sustainable, contributing to more resilient and diversified protein production systems.

Background fields: bioprocess design, simulation, and techno-economic evaluation, life cycle assessments

Project title: High-resolution waste-derived novel biotechnology feedstock-host background data generation to enable sustainability assessments

Ralph's project focuses on developing a high-resolution Resource and Sustainability Atlas for emerging single-cell protein production. The project links regional feedstock availability, feedstock characteristics, conversion potential, techno-economic performance, environmental impacts, and social/socioeconomic indicators. By integrating resource assessment with sustainability analysis, the Atlas aims to support the identification and comparison of promising feedstock-to-protein pathways, including waste-derived resources and CO₂-based single-cell protein systems. The final output will contribute to the FrameBio Resource Atlas, impact database, and dashboard for sustainable novel food and protein production.

Background fields: single-cell protein; waste-derived feedstocks; CO₂-based bioproduction; resource assessment; life cycle assessment; techno-economic analysis; GIS and spatial modelling; sustainability assessment; uncertainty analysis; database development

Project title: Comprehensive water scarcity method development

Alka's research focuses on improving water-related impact assessment in sustainable food systems using Life Cycle Assessment (LCA). The work investigates green water flows and their potential contribution to water deprivation impacts, addressing current limitations in Life Cycle Impact Assessment (LCIA) methods. The research will support more comprehensive environmental assessments of emerging food production systems, including alternative protein routes such as single-cell protein production.

Background fields: life cycle assessment (LCA), characterisation factor (CF) model development, PFAS treatment technologies, chemical and process engineering

Project title: Advancing Water Scarcity Assessment of Single-Cell Protein Production through Geographic Information Systems

The PhD project aims to improve water scarcity assessment in Life Cycle Assessment (LCA) by developing a GIS-integrated methodology that incorporates spatial and temporal variability in both inventory data and impact assessment. This framework will be applied to Single-Cell Protein production to evaluate how production location, timing, and local environmental conditions influence water scarcity impacts and overall environmental performance. The project will support more spatially explicit and context-sensitive assessments of alternative protein systems for water scarcity and possibly further indicators.

Background fields: climate change, geographic information systems, environmental and climate modelling, spatial analysis, sustainability and impact assessment

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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

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Project title: Multi-Criteria Decision Making (MCDM) and Multi-Objective Optimization (MOO) to Guide the Production of Single-Cell Proteins (SCPs)

With a background in biotechnology and bioprocess engineering, I will develop an integrated decision-support framework for the sustainable production of single-cell proteins (SCPs). The framework will incorporate environmental, technical, and socio-economic indicators into a unified Multi-Criteria Decision Making (MCDM) and Multi-Objective Optimization (MOO) approach. By combining multi-criteria ranking with multi-objective optimization, the framework will support balanced, data-driven decision-making and help identify sustainable SCP production strategies while accounting for potential trade-offs among environmental performance, process efficiency, economic feasibility, and other relevant objectives.

Background fields: biotechnology; bioprocess engineering; life cycle assessment (LCA); sustainability assessment; sustainable bioprocessing; decision-support frameworks.