Introduction to Computational/Quantitative Synthetic Biology

Learning Outcomes

After successfully completing the lectures and computational laboratory sessions of the course, postgraduate students will be able to:

– In terms of knowledge and understanding

• analyse the structure, dynamics and modular organization of biological networks, metabolic pathways, and genetic circuits.
• evaluate computational, bioinformatics, and machine learning approaches used for the design, modelling and optimization of biological systems.
• design and evaluate synthetic genetic circuits (promoters, toggle switches, logic gates, biosensors) using quantitative modelling frameworks
• predict and evaluate protein structures and interaction networks using state-of-the-art computational tools including AlphaFold-era methods

– In terms of skills

• analyse and interpret data generated from high-throughput technologies, such as Next-Generation Sequencing and multi-omics approaches.
• use computational biology and machine-learning tools to analyse, simulate, and optimise synthetic biological systems
• apply core bioinformatics algorithms (dynamic programming, BLAST, HMMs, Bayesian methods) to analyse and annotate biological sequences
• execute UNIX shell and R-based pipelines for processing and integrating multi-omics data sets (genomics, transcriptomics, proteomics, metabolomics)
• perform differential expression and differential abundance analyses and critically assess statistical assumptions and multiple-testing corrections
• mine metagenomic data sets for novel enzymatic functions through assembly, binning, and functional gene annotation workflows
• integrate computational results across multiple omics layers and communicate findings in a collaborative project setting

– In terms of competencies

• design and independently conduct research projects in metagenomics and synthetic biology, applying modern experimental, computational, and automated methodologies.
• 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.
• collaborate effectively within interdisciplinary teams combining biology, engineering, computer science, and chemistry.
• communicate scientific results and technological applications effectively to both specialist and non-specialist audiences.

Module Syllabus

Theory

• Intro to Computational Biology: biological sequence analysis, homology, dynamic programming, BLAST, hidden Markov models, Bayesian statistics
• SynBio and biological sequences theory: Synthetic Circuit Design and Modelling – quantitative design of synthetic promoters, toggle switches, logic gates, and biosensors; model-guided design cycles
• Introduction to UNIX and R scripting: UNIX environment, shell scripting, R programming basics, file parsing, data processing pipelines, collaborative working (GitHub)
• Statistical concepts for biological analysis: variability, bias, independence, distributions, multiple hypothesis testing, outliers, missing data, clustering
• Genomics Data Processing and Integration: high-throughput sequencing platforms, quality control, genome assembly and structural/functional annotation
• Transcriptomics: experimental design, data QA and QC, statistical models for differential expression analysis
• Proteomics, Metabolomics, and multi-omics integration: data strategies; differential analysis frameworks (partial least squares statistics)
• Protein Structure Prediction and Dynamics: computational methods (AlphaFold era), modelling protein dynamics, evolutionary information
• Protein Interaction Networks: reconstruction and analysis of protein–ligand, protein–protein interaction and regulatory networks; network topology and motifs
• Metagenome mining for novel functions: metagenome assembly, binning, functional annotation, and mining for novel enzymatic activities; applications in Synthetic Biology and pathway engineering
• Synthetic Circuit Design and Modelling applications: quantitative design of synthetic promoters, toggle switches, logic gates, and biosensors; model-guided design cycles
• Student Presentations and Integration: student project presentations; integrative discussion of synthetic biology challenges and future directions

Computer Practicals

• UNIX/R scripting practical: shell commands, file parsing, and data processing pipelines
• Genomics/Transcriptomics data analysis: quality control, read alignment, and differential expression with R/DESeq2
• Proteomics and metabolomics data analysis: data normalisation, PLS-DA, and pathway enrichment
• Synthetic circuit design and modelling: quantitative simulation of synthetic genetic circuits (promoters, toggle switches, logic gates) using Python-based modelling tools
• Protein structure and interaction analysis: AlphaFold predictions, molecular visualisation, and network reconstruction

Suggested Bibliography

• “An Introduction to Bioinformatics Algorithms”, Jones, N.C., 2004. ISBN: 978-0-262-10106-6
• “Bioinformatics Algorithms: An Active Learning Approach”, P. Compeau & P. Pevzner, Active Learning Publishers, 2018. ISBN: 978-0-9903746-3-3
• “Bioinformatics Data Skills”, V. Buffalo, O’Reilly Media, 2015. ISBN: 978-1-449-36737-4
• “Synthetic Biology: A Primer”, P. S. Freemont & R. I. Kitney (Eds.), Imperial College Press, 2016. ISBN: 978-1-783-26879-5
• “Statistical Methods in Bioinformatics: An Introduction”, W. J. Ewens & G. R. Grant, 2nd Edition, Springer, 2005. ISBN: 978-0-387-40082-2
• Course Lecture Notes, Slides, and Computational Lab Tutorials (distributed via the course e-class platform)