Metabolic and Bioprocess Engineering

Learning Outcomes

Knowledge and Understanding

• Explain at an advanced level the molecular, genetic, biochemical and engineering principles that govern the design and function of synthetic biological systems.
• Analyse the structure, dynamics and modular organization of biological networks and metabolic pathways.
• Understand and critically evaluate modern engineering technologies, and automated biology platforms.
• Evaluate computational, bioinformatics, and machine learning approaches used for the design, modelling and optimization of biological systems.

Skills

• Design and implement strategies for constructing synthetic genetic circuits, metabolic networks, and engineered biological systems.
• Analyse and interpret data generated from high-throughput technologies such as multi-omics approaches.
• Use computational biology and machine-learning tools to analyse, simulate, and optimise synthetic biological systems.
• Design and evaluate applications of synthetic biology in biomedicine, industrial biotechnology, agriculture, and the environment.
• Apply the Design-Build-Test-Learn (DBTL) framework to the development and optimization of synthetic biological systems.

Competencies

• Critically evaluate the scientific literature and integrate new research findings into the design of biological systems.
• Make informed decisions when addressing complex and unpredictable research or technological challenges.
• Integrate principles of responsible innovation, bioethics, biosafety, and biosecurity into research and the development of biotechnological applications.
• Collaborate effectively within interdisciplinary teams combining biology, engineering, computer science, and chemistry.

Module Syllabus

• Fundamentals of Metabolic and Bioprocess Engineering, Metabolic engineering role in transition from fossil resources to a bio-based economy
• Design-build-test-learn cycle of metabolic engineering
• Design and Engineering of Microbial Cell Factories
• Metabolic network analysis, constraint-based modelling of metabolism
• Genome-Scale Metabolic Modeling
• Fundamentals of Biochemical Engineering
• Microbial Growth Kinetics and Strain Development
• Fermentation optimization and Scale-Up
• Synthetic Microbial Communities and bioprocess optimization – case studies 
• Sustainability assessment (TEA and LCA) in metabolic engineering
• Functional metagenomics, principles, applications and examples of successful discovery of novel proteins

Lab Practicals

Comparative Microbial Cultivation and Carbon Source Utilization

Students will investigate the effect of different carbon sources on microbial growth and metabolism using a model organism (e.g. bacterium or yeast). Cultivations will be performed using conventional substrates (e.g. glucose) and alternative or renewable carbon sources (e.g. glycerol or biomass-derived substrates). Students will monitor key parameters including optical density (growth), substrate consumption, and product formation. Data analysis will focus on calculating yields, biomass productivity, and carbon conversion efficiency. The laboratory exercise will emphasize the role of substrate choice in the transition from fossil-based to bio-based production systems, linking experimental results to concepts of sustainability and industrial biotechnology.

Bioreactor Operation, Monitoring, and Mass Balance Analysis

In this exercise, students will operate a bench-scale bioreactor (or a virtual/simulated system) to cultivate a microbial culture under controlled conditions. They will monitor and control critical process parameters such as pH, dissolved oxygen, temperature, and agitation. Students will collect time-course data and perform mass balance calculations to evaluate substrate utilization, biomass formation, and product generation. The practical will introduce fundamental concepts of bioprocess control, reactor performance, and process stability, while highlighting challenges associated with scaling laboratory processes to industrial systems.

Microbial Growth Kinetics and Engineering of Production Strains

Students will analyse microbial growth kinetics by generating growth curves under defined conditions and determining key parameters such as maximum specific growth rate (μmax) and substrate affinity constant (Ks). Experimental data will be fitted to established growth models (e.g. Monod kinetics). In parallel, students will work with an engineered microbial strain expressing a heterologous enzyme or metabolic pathway. They will evaluate gene expression, metabolic burden, and production performance, comparing engineered and non-engineered strains. The practical integrates concepts of strain development, metabolic engineering, and process optimization.

Suggested Bibliography

• Stephanopoulos G, Aristidou AA, Nielsen J (1998) Metabolic engineering: principles and methodologies. Academic Press, San Diego.
• Nielsen J, Keasling JD (2016) Engineering cellular metabolism. Cell 164:1185–1197.
• Shuler ML, Kargi F (2017) Bioprocess engineering: basic concepts. 3rd ed. Prentice Hall, Upper Saddle River.
• Lee SY, Kim HU (2015) Systems strategies for developing industrial microbial strains. Nature Biotechnology 33:1061–1072.
• Nielsen J, Larsson C, van Maris A, Pronk J (2013) Metabolic engineering of yeast for production of fuels and chemicals. Current Opinion in Biotechnology 24:398–404.
• Großkopf & Soyer, (2014) Synthetic Microbial Communities. Current Opinion in Microbiology 18: 72-77
• Johns et al., (2016) Principles for designing synthetic microbial communities. Current Opinion in Microbiology 31: 146-153
• Ferrer M., et al., (2016) Estimating the success of enzyme bioprospecting through metagenomics: current status and future trends. Microbial Biotechnology 9(1), 22–34
• Charles, Lilles, Sessitsch (2017) Functional Metagenomics: Tools and Applications. Springer